OpenAI Releases First Open-Weight AI Models Since 2019: A Shift in Strategy and Implications for the AI Landscape

OpenAI launches reasoning LLM that you can download and tweak

OpenAI has launched a large language model (LLM) that lives up to the company's name. Known as gpt-oss, it is the first 'reasoning' artificial intelligence (AI) from the firm that is open-weight, meaning that researchers will be able to download it and customize it. The firm, based in San Francisco, California, detailed the system in a blog post and a technical description on 5 August. On some tasks, gpt-oss performs almost as well as the firm's most advanced models. The LLM is available in two sizes, both of which can be run locally and offline -- the smaller of them on a single laptop -- rather than requiring cloud computing or an online interface. This means they can be used to analyse -- or be trained further on -- sensitive data that can't be transferred outside a given network. "I'm very excited," says Simon Frieder, a mathematician and computer scientist at the University of Oxford, UK. "The competition between open-source large language models is already strong, and this will make the competition even fiercer, which benefits the entire research community." The release of gpt-oss comes at a time when powerful open-weight models from Chinese firms, such as Hangzhou-based DeepSeek and Beijing-based Moonshot AI, are gaining traction among researchers. Chinese open models already perform better than US-developed ones such as Llama (from Meta, based in Menlo Park, California) and are also poised to overtake them in terms of number of downloads, according to an analysis by Nathan Lambert, a machine-learning researcher at the Allen Institute for AI in Seattle, Washington, which was carried out before gpt-oss was released. Last month, the administration of US president Donald Trump highlighted open-weight AI models as being "essential for academic research" in its AI Action Plan. OpenAI's decision to launch an open model has been long in the works and is not a response to the success of Chinese models, said Greg Brockman, one of the firm's founders, who spoke to journalists ahead of the release of gpt-oss."It was never a thing that we didn't want to do," he added. All models come with biases, so diversity among their creators benefits users, says Frieder. "Having a new top-performing model from a Western company is a step in the direction of levelling the playing field in terms of which companies dominate the open-weight model space," he says. Until now, OpenAI has largely published proprietary models, the exception being GPT-2, a 2019 LLM released by the firm three years before it launched its popular ChatGPT chatbot. The latest open models are 'reasoners' trained to produce output using a step-by-step process that mimics thought. Previous reasoning models, such as OpenAI's o3, have been shown to excel on science and mathematics problems. As well as using them to write computer code and review scholarly literature, scientists are experimenting with using LLMs as AI 'co-scientists' in the hope of accelerating research. In performance, OpenAI's open models seem to be close to the firm's most advanced, pay-to-access AIs -- the main differences being the open models' smaller sizes and their being text-only (they do not handle images or video). Gpt-oss can browse the web, execute code and operate software, and it outperforms similarly-sized open models on reasoning tasks, says the firm. On the AIME 2025 benchmark, which tasks AIs with solving challenging mathematics problems, the gpt-oss models score better than the best existing open models, such as DeepSeek's R1, and one of the two is on par with the leading open competitor on Humanity's Last Exam, a 3,000-question test that covers expert-level knowledge across a range of subjects. In May, OpenAI's chief scientist, Jakub Pachocki, told Nature that the firm was unlikely to release its most cutting-edge models as open-weight out of safety concerns. Doing so would mean relinquishing control of how the Al is used and developed. In safety tests, outlined in an accompanying paper on 5 August, researchers at the company reported that, when further trained to act maliciously, gpt-oss did not substantially increase the risks compared with existing open and proprietary models in domains such as biosecurity and cybersecurity. Although the gpt-oss models are open-weight, they are not fully open-source in that details of the training data are not included, for example. This means that researchers cannot recreate these models from scratch. Open-weight models have "more potential for personalization", says Carrie Wright, a data scientist at the Fred Hutchinson Cancer Center in Seattle, Washington. But Wright notes that researchers have long used many tools besides OpenAI's GPT models. OpenAI's own analysis suggests that its latest models, in some cases, generate more hallucinations -- instances of the AI inventing information -- than do previous iterations, she says. "Outputs still require checking," adds Elizabeth Humphries, a data scientist also at the Fred Hutchinson Cancer Center. "Using these tools to support our work, instead of to replace our work, is typically going to work out much better."

OpenAI launches two 'open' AI reasoning models | TechCrunch

OpenAI announced Tuesday the launch of two open-weight AI reasoning models with similar capabilities to its o-series. Both are freely available to download from the online developer platform, Hugging Face, the company said, describing the models as "state-of-the-art" when measured across several benchmarks for comparing open models. The models come in two sizes: a larger and more capable gpt-oss-120b model that can run on a single Nvidia GPU, and a lighter-weight gpt-oss-20b model that can run on a consumer laptop with 16GB of memory. The launch marks OpenAI's first 'open' language model since GPT-2, which was released more than five years ago. In a briefing, OpenAI said its open models will be capable of sending complex queries to AI models in the cloud, as TechCrunch previously reported. That means if OpenAI's open model is not capable of a certain task, such as processing an image, developers can connect the open model to one of the company's more capable closed models. While OpenAI open-sourced AI models in its early days, the company has generally favored a proprietary, closed-source development approach. The latter strategy has helped OpenAI build a large business selling access to its AI models via an API to enterprises and developers. However, CEO Sam Altman said in January he believes OpenAI has been "on the wrong side of history" when it comes to open sourcing its technologies. The company today faces growing pressure from Chinese AI labs -- including DeepSeek, Alibaba's Qwen, and Moonshot AI -- which have developed several of the world's most capable and popular open models. (While Meta previously dominated the open AI space, the company's Llama AI models have fallen behind in the last year.) In July, the Trump Administration also urged U.S. AI developers to open source more technology to promote global adoption of AI aligned with American values. With the release of gpt-oss, OpenAI hopes to curry favor with developers and the Trump Administration alike, both of which have watched the Chinese AI labs rise to prominence in the open source space. "Going back to when we started in 2015, OpenAI's mission is to ensure AGI that benefits all of humanity," said OpenAI CEO Sam Altman in a statement shared with TechCrunch. "To that end, we are excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit." OpenAI aimed to make its open model a leader among other open-weight AI models, and the company claims to have done just that. On Codeforces (with tools), a competitive coding test, gpt-oss-120b and gpt-oss-20b score 2622 and 2516, respectively, outperformed DeepSeek's R1 while underperforming o3 and o4-mini. On Humanity's Last Exam, a challenging test of crowd-sourced questions across a variety of subjects (with tools), gpt-oss-120b and gpt-oss-20b score 19% and 17.3%, respectively. Similarly, this underperforms o3 but outperforms leading open models from DeepSeek and Qwen. Notably, OpenAI's open models hallucinate significantly more than its latest AI reasoning models, o3 and o4-mini. Hallucinations have been getting more severe in OpenAI's latest AI reasoning models, and the company previously said it doesn't quite understand why. In a white paper, OpenAI says this is "expected, as smaller models have less world knowledge than larger frontier models and tend to hallucinate more." OpenAI found that gpt-oss-120b and gpt-oss-20b hallucinated in response to 49% and 53% of questions on PersonQA, the company's in-house benchmark for measuring the accuracy of a model's knowledge about people. That's more than triple the hallucination rate of OpenAI's o1 model, which scored 16%, and higher than its o4-mini model, which scored 36%. OpenAI says its open models were trained with similar processes to its proprietary models. The company says each open model leverages mixture-of-experts (MoE) to tap fewer parameters for any given question, making it run more efficiently. For gpt-oss-120b, which has 117 billion total parameters, OpenAI says the model only activates 5.1 billion parameters per token. The company also says its open model was trained using high-compute reinforcement learning (RL) -- a post-training process to teach AI models right from wrong in simulated environments using large clusters of Nvidia GPUs. This was also used to train OpenAI's o-series of models, and the open models have a similar chain-of-thought process in which they take additional time and computational resources to work through their answers. As a result of the post-training process, OpenAI says its open AI models excel at powering AI agents, and are capable of calling tools such as web search or Python code execution as part of its chain-of-thought process. However, OpenAI says its open models are text-only, meaning they will not be able to process or generate images and audio like the company's other models. OpenAI is releasing gpt-oss-120b and gpt-oss-20b under the Apache 2.0 license, which is generally considered one of the most permissive. This license will allow enterprises to monetize OpenAI's open models without having to pay or obtain permission from the company. However, unlike fully open source offerings from AI labs like AI2, OpenAI says it will not be releasing the training data used to create its open models. This decision is not surprising given that several active lawsuits against AI model providers, including OpenAI, have alleged that these companies inappropriately trained their AI models on copyrighted works. OpenAI delayed the release of its open models several times in recent months, partially to address safety concerns. Beyond the company's typical safety policies, OpenAI says in a white paper that it also investigated whether bad actors could fine-tune its gpt-oss models to be more helpful in cyber attacks or the creation of biological or chemical weapons. After testing from OpenAI and third-party evaluators, the company says gpt-oss may marginally increase biological capabilities. However, it did not find evidence that these open models could reach its "high capability' threshold for danger in these domains, even after fine-tuning. While OpenAI's model appears to be state-of-the-art among open models, developers are eagerly awaiting the release of DeepSeek R2, its next AI reasoning model, as well as a new open model from Meta's new superintelligence lab.

OpenAI has finally released open-weight language models

That's particularly notable at a time when Meta, which had previously dominated the American open-model landscape with its Llama models, may be reorienting toward closed releases -- and when Chinese open models, such as DeepSeek's offerings, Kimi K2, and Alibaba's Qwen series, are becoming more popular than their American competitors. "The vast majority of our [enterprise and startup] customers are already using a lot of open models," said Casey Dvorak, a research program manager at OpenAI, in a media briefing about the model release. "Because there is no [competitive] open model from OpenAI, we wanted to plug that gap and actually allow them to use our technology across the board." The new models come in two different sizes, the smaller of which can theoretically run on 16 GB of RAM -- the minimum amount that Apple currently offers on its computers. The larger model requires a high-end laptop or specialized hardware. Open models have a few key use cases. Some organizations may want to customize models for their own purposes or save money by running models on their own equipment, though that equipment comes at a substantial upfront cost. Others -- such hospitals, law firms, and governments -- might need models that they can run locally for data security reasons. OpenAI has facilitated such activity by releasing its open models under a permissive Apache 2.0 license, which allows the models to be used for commercial purposes. Nathan Lambert, post-training lead at the Allen Institute for AI, says that this choice is commendable: Such licenses are typical for Chinese open-model releases, but Meta released its Llama models under a bespoke, more restrictive license. "It's a very good thing for the open community," he says. Researchers who study how LLMs work also need open models, so that they can examine and manipulate those models in detail. "In part, this is about reasserting OpenAI's dominance in the research ecosystem," says Peter Henderson, an assistant professor at Princeton University who has worked extensively with open models. If researchers do adopt gpt-oss as new workhorses, OpenAI could see some concrete benefits, Henderson says -- it might adopt innovations discovered by other researchers into its own model ecosystem. More broadly, Lambert says, releasing an open model now could help OpenAI reestablish its status in an increasingly crowded AI environment. "It kind of goes back to years ago, where they were seen as the AI company," he says. Users who want to use open models will now have the option to meet all their needs with OpenAI products, rather than turning to Meta's Llama or Alibaba's Qwen when they need to run something locally. The rise of Chinese open models like Qwen over the past year may have been a particularly salient factor in OpenAI's calculus. An employee from OpenAI emphasized at the media briefing that the company doesn't see these open models as a response to actions taken by any other AI company, but OpenAI is clearly attuned to the geopolitical implications of China's open-model dominance. "Broad access to these capable‬‭ open-weights models created in the US helps expand democratic AI rails," the company wrote in a blog post announcing the models' release.

OpenAI Just Released Its First Open-Weight Models Since GPT-2

The models, gpt-oss-120b and gpt-oss-20b, represent a major shift for the AI company. OpenAI just dropped its first open-weight models in over five years. The two language models, gpt-oss-120b and gpt-oss-20b, can run locally on consumer devices and be fine-tuned for specific purposes. For OpenAI, they represent a shift away from its recent strategy of focusing on proprietary releases, as the company moves towards a wider, and more open, group of AI models that are available for users. "We're excited to make this model, the result of billions of dollars of research, available to the world to get AI into the hands of the most people possible," said OpenAI CEO Sam Altman in an emailed statement. Both gpt-oss-120b and gpt-oss-20b are officially available to download for free on Hugging Face, a popular hosting platform for AI tools. The last open-weight model released by OpenAI was GPT-2, back in 2019. What sets apart an open-weight model is the fact that its "weights" are publicly available, meaning that anyone can peek at the internal parameters to get an idea of how it processes information. Rather than undercutting OpenAI's proprietary models with a free option, cofounder Greg Brockman sees this release as "complementary" to the company's paid services, like the application programming interface currently used by many developers. "Open-weight models have a very different set of strengths," said Brockman in a briefing with reporters. Unlike ChatGPT, you can run a gpt-oss model without a connection to the internet and behind a firewall. Both gpt-oss models use chain-of-thought reasoning approaches, which OpenAI first deployed in its o1 model last fall. Rather than just giving an output, this approach has generative AI tools go through multiple steps to answer a prompt. These new text-only models are not multimodal, but they can browse the web, call cloud-based models to help with tasks, execute code, and navigate software as an AI agent. The smaller of the two models, gpt-oss-20b, is compact enough to run locally on a consumer device with more than 16 GB of memory. The two new models from OpenAI are available under the Apache 2.0 license, a popular choice for open-weight models. With Apache 2.0, models can be used for commercial purposes, redistributed, and included as part of other licensed software. Open-weight model releases from Alibaba's Qwen as well as Mistral also operate under Apache 2.0. Publicly announced in March, the release of these open models was initially delayed for further safety testing. Releasing an open-weight model is potentially more dangerous than a closed-off version since it removes barriers around who can use the tool, and anyone can try to fine-tune a version of gpt-oss for unintended purposes. In addition to the evaluations OpenAI typically runs on its proprietary models, the startup customized the open-weight option to see how it could potentially be misused by a "bad actor" who downloads the tool. "We actually fine-tuned the model internally on some of these risk areas," said Eric Wallace, a safety researcher at OpenAI, "and measured how high we could push them." In OpenAI's tests, the open-weight model did not reach a high level of risk, as measured by its preparedness framework.

OpenAI's New Models Aren't Really Open: What to Know About Open-Weights AI

Despite the company's name, OpenAI hasn't dropped an open version of its AI models since GPT-2 in 2019. That changed on Tuesday, as CEO Sam Altman shared two new open-weights, reasoning AI models, named gpt-oss-120b (120 billion parameters) and gpt-oss-20b (20 billion parameters). If open-weights is a new piece of AI jargon to you, don't worry. In the simplest possible terms, open-weights is a category of AI models that power products like chatbots, image and video generators. But they are philosophically different from the technology underpinning the AI tools you might use now. ChatGPT, Gemini and Copilot are all powered by closed models, which means we have no real insights into how those black box machines work. Open-weights models give us a peek at the mechanical wizard behind the curtain, so to speak. You don't need to be a developer or machine learning expert to understand how these open models work or even to run them yourself. Here's everything you need to know about open-weights and open-source AI models. All AI models have weights, which are characteristics or elements. Models are trained to give certain connections more weight, or value. An open-weights model does exactly what its name implies -- the weights are publicly available, as defined by the Federal Trade Commission. Developers can see these weights and how they're used in the creation of AI models. "Arguably, the most valuable thing in large [language] models is actually the weights. You can do a lot if you have the weights, which is somewhat different from traditional software," Omar Khattab, assistant professor of computer science at MIT and researcher in its computer science and artificial intelligence lab (CSAIL), told CNET. For example, a chatbot is built to be really good at predicting the next logical word in a sentence. It's trained to string together words in its outputs that frequently show up next to each other in its training data, presumably in a logical order. Words that show up next to each other more frequently can be given more weight than words that don't often appear next to each other. These weights are just numbers but open-weights models also come with a map. "In open-weights [models], you get the weights, which are these numbers, and you get how to map those weights into the neural network structure, so the layers of the neural network, in order to actually be able to run it," said Khattab. The architecture of the model shows how a company structures its models, which is "incredibly valuable." Open-weights models are primarily aimed at developers, who can integrate the model into existing projects, like helping to build AI agents. For the "committed hobbyist," as Khattab put it, you can use the specs to run the model locally on your laptop, which could help alleviate privacy concerns that come, for example, with using AI through a company's mobile app. Researchers will also have a clearer look at how the AI works internally. The new open-weights models come in two sizes, 120 billion parameters (128 experts and 128k context window) and 20 billion parameters (32 experts but the same 128k context window). Experts refer to the number of sub-neural networks a model has, and context windows describe how much information a model can process and include in its responses. Bigger numbers for both indicate a model is capable of more sophisticated answers and has more firepower. In terms of performance, OpenAI is reporting that the 120B model "achieved near-parity" with its latest reasoning model, o4-mini on core reasoning benchmarks while running on a single 80 gigabyte GPU. The 20B open-weights model performed similarly to o3-mini and ran on a 16 gigabyte device -- meaning, this smaller open-weights model could be run fairly well on laptops and some smartphones. (Like all AI models run locally, your speed will depend on your device's firepower.) The models will be available under the Apache 2.0 license, a type of open-source-friendly license. You can check out the more in-depth specs in the model card and paper on safety training, get tips from OpenAI's developer guidelines and see the weights for yourself now on HuggingFace and Github. Open-weights models are related to open-source AI, but they aren't exactly the same. Open source as a concept refers to software that has no proprietary owners, whose source code is publicly available and can be used by most anybody under open-source licenses. The Open Source Initiative, a nonprofit that advocates for open-source software, defines open-source AI as "a system made available under terms that grant users the freedom to use, study, modify and share [it]." An open-weights AI model isn't the same as an open-source model. One way to think about the difference between the two is like with baking, Suba Vasudevan, chief operating officer at Mozilla.org and senior vice president at Mozilla corporation, told CNET. "An open-weights model is someone giving you this baked cake and saying, 'Oh, it's made of flour, sugar and eggs.' Those are the weights. But if someone gave you the entire recipe, and all the instructions and the exact details of how much of each ingredient went into it, that's open source," said Vasudevan. For open-weights models, the types of things not disclosed are the data the model was trained on and the code used to train it. Training data is a point of contention between AI companies and the humans creating content; AI companies are ravenous for high-quality, human-generated content to refine and improve their models. Some companies collect this data through licensing agreements, but some publishers and creators have filed lawsuits alleging that AI companies are illegally acquiring their copyrighted content. (Disclosure: Ziff Davis, CNET's parent company, in April filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.) Training data, regardless of its origin, is one of the most valuable things an AI company has. But it likely won't be included in any open-weights model release. "I think because of sheer scale [of data], because of liability, because a lot of it is licensed and you are not supposed to share it, I think it's probably fair to assume that no companies, for-profit at least, are going to be releasing that anytime soon, maybe ever," said Khattab. Truly open-source AI comes with more publicly available information, Vasudevan said. Open-weights models can be harder to retrain or inspect for bias without the additional information. "You're still a fair bit in the dark about how it was built or what data shaped it," Vasudevan said. These differences between open-source, open-weights and closed models likely won't affect your average experience using an AI chatbot. But they're important qualities for developers to consider when selecting and using certain AI models, and they're important more broadly for us to understand the technology that is infiltrating our online lives. There's no guarantee that the inner workings of its open-weights models will reflect what's inside its closed models, Khattab said. But for a company whose product is synonymous with gen AI, any light shed on how it works is sure to have an impact on the people who use it and those who design it behind the scenes. As people start to get into the weeds with the new models, we'll learn more and see what effect it has on the industry going forward. Overall, the underlying philosophy of open source is that technology gets better when more people can access it, like academics who can study it and security experts who can expose weaknesses. "You have to let people build, scale and innovate and be able to poke holes and make it better," said Vasudevan.

OpenAI returns to its open-source roots with new open-weight AI models, and it's a big deal

We all know AI relies on open-source software, but most of the big AI companies avoid opening their code or their large language model (LLM) weights. Today, things have changed. OpenAI, the artificial intelligence titan behind ChatGPT, announced a landmark return to its open-source origins. Open-weight models enable anyone to download, examine, run, or fine-tune the LLM, and they eliminate the need to rely on remote cloud APIs or expose in-house sensitive data to external services. OpenAI has not, however, released the training data used for these models because of legal and safety concerns. That will not please open-source AI purists, but developers worldwide are already putting the two models to the test. This change contrasts with OpenAI's approach over the past five years. The business has prioritized proprietary releases fueled by massive Microsoft investments and lucrative API deals. After all, you can't hope to become a trillion-dollar AI company without maximizing your profits. On the other hand, open source has consistently demonstrated that when code is developed openly, everyone, including the company that releases the code, benefits. The gpt-oss-120b model targets high-performance servers and desktops with beefed-up specifications -- 60 GB of VRAM and multiple GPUs -- while the gpt-oss-20b version is compact enough for most laptops. You can download the models from Hugging Face or GitHub. In both cases, your hardware must run MacOS or Linux specifically, with MacOS 11 Big Sur or later, or Linux with Ubuntu 18.04 or later to run the programs. It could also work on Windows Subsystem for Linux (WSL) 2.0 on high-powered Windows systems. OpenAI says, "The gpt-oss-120b model achieves near-parity with OpenAI o4-mini on core reasoning benchmarks, while running efficiently on a single 80 GB GPU. The gpt-oss-20b model delivers similar results to OpenAI o3‑mini on common benchmarks and can run on edge devices with just 16 GB of memory." Also: People are using ChatGPT to write their text messages - here's how you can tell So, how good is it? AI expert Nate Jones has kicked its tires and reports, "This one is specifically aimed at retaking American dominance in open-source models now that Llama has dropped the ball. Early tests indicate a higher than usual risk of hallucination, but the power of the model is real and continues to underline how quickly AI is progressing. I'll be watching for how quickly these models get picked up on Hugging Face by developers (who are hard to spin)." The models are licensed under Apache 2.0, one of the most permissive open licenses. This enables enterprises and developers to use, modify, and monetize the technology without restrictive terms, unlike Meta's not-really open-source Llama LLMs. Both models employ a mixture-of-experts (MoE) architecture. This offers robust reasoning capabilities while being optimized for efficiency and tool usage. Programmers will be interested in its code execution capabilities, while writers and researchers will find its inclusion of web search as part of their thought process interesting. On the other hand, early reports show very high levels of hallucinations. Additionally, both models are limited to processing text. Also: My go-to LLM tool just dropped a super simple Mac and PC app for local AI Why has OpenAI made this move? The company explicitly stated that these open releases aim to lower barriers in emerging markets and among smaller organizations. The business has also noticed that the Chinese open-source DeepSeek, which was released in January and immediately made waves thanks to its speed, power, and the fact that it was open source. As Altman said shortly after DeepSeek caught everyone's attention in a Reddit "Ask Me Anything," he believes OpenAI has been "on the wrong side of history" about open-sourcing its software.

OpenAI Releases Open-Weight Models After DeepSeek's Success

OpenAI is releasing a pair of open and freely available artificial intelligence models that can mimic the human process of reasoning, months after China's DeepSeek gained global attention with its own open AI software. The two models, called GPT-oss-120b and GPT-oss-20b, will be available on AI software hosting platform Hugging Face and can produce text -- but not images or videos -- in response to user prompts, OpenAI said on Tuesday. These models can also carry out complex tasks like writing code and looking up information online on a user's behalf, the company said.

OpenAI releases open-weight reasoning models optimized for running on laptops

SAN FRANCISCO, Aug 5 (Reuters) - OpenAI said on Tuesday it has released two open-weight language models that excel in advanced reasoning and are optimized to run on laptops with performance levels similar to its smaller proprietary reasoning models. An open-weight language model's trained parameters or weights are publicly accessible, which can be used by developers to analyze and fine-tune the model for specific tasks without requiring original training data. "One of the things that is unique about open models is that people can run them locally. People can run them behind their own firewall, on their own infrastructure," OpenAI co-founder Greg Brockman said in a press briefing. Open-weight language models are different from open-source models, which provide access to the complete source code, training data and methodologies. The landscape of open-weight and open-source AI models has been highly contested this year. For a time, Meta's (META.O), opens new tab Llama models were considered the best, but that changed earlier this year when China's DeepSeek released a powerful and cost-effective reasoning model, while Meta struggled to deliver Llama 4. The two new OpenAI models are the first open models OpenAI has released since GPT-2, which was released in 2019. OpenAI's larger model, gpt-oss-120b, can run on a single GPU, and the second, gpt-oss-20b, is small enough to run directly on a personal computer, the company said. OpenAI said the models have similar performance to its proprietary reasoning models called o3-mini and o4-mini, and especially excel at coding, competition math and health-related queries. The models were trained on a text-only dataset which in addition to general knowledge, focused on science, math and coding knowledge. OpenAI did not release benchmarks comparing the open-weight models to competitors' models such as the DeepSeek-R1 model. Microsoft-backed OpenAI, currently valued at $300 billion, is currently raising up to $40 billion in a new funding round led by Softbank Group (9984.T), opens new tab. Reporting by Anna Tong in San Francisco; Editing by Edwina Gibbs Our Standards: The Thomson Reuters Trust Principles., opens new tab * Suggested Topics: * Artificial Intelligence Anna Tong Thomson Reuters Anna Tong is a correspondent for Reuters based in San Francisco, where she reports on the technology industry. She joined Reuters in 2023 after working at the San Francisco Standard as a data editor. Tong previously worked at technology startups as a product manager and at Google where she worked in user insights and helped run a call center. Tong graduated from Harvard University.

OpenAI's first new open-weight LLMs in six years are here

For the first time since GPT-2 in 2019, OpenAI is releasing new open-weight large language models. It's a major milestone for a company that has increasingly been accused of forgoing its original stated mission of "ensuring artificial general intelligence benefits all of humanity." Now, following multiple delays for additional safety testing and refinement, gpt-oss-120b and gpt-oss-20b are available to download from Hugging Face. Before going any further, it's worth taking a moment to clarify what exactly OpenAI is doing here. The company is not releasing new open-source models that include the underlying code and data the company used to train them. Instead, it's sharing the weights -- that is, the numerical values the models learned to assign to inputs during their training -- that inform the new systems. According to Benjamin C. Lee, professor of engineering and computer science at the University of Pennsylvania, open-weight and open-source models serve two very different purposes. "An open-weight model provides the values that were learned during the training of a large language model, and those essentially allow you to use the model and build on top of it. You could use the model out of the box, or you could redefine or fine-tune it for a particular application, adjusting the weights as you like," he said. If commercial models are an absolute black box and an open-source system allows for complete customization and modification, open-weight AIs are somewhere in the middle. OpenAI has not released open-source models, likely since a rival could use the training data and code to reverse engineer its tech. "An open-source model is more than just the weights. It would also potentially include the code used to run the training process," Lee said. And practically speaking, the average person wouldn't get much use out of an open-source model unless they had a farm of high-end NVIDIA GPUs running up their electricity bill. (They would be useful for researchers looking to learn more about the data the company used to train its models though, and there are a handful of open-source models out there like Mistral NeMo and Mistral Small 3.) With that out of the way, the primary difference between gpt-oss-120b and gpt-oss-20b is how many parameters each one offers. If you're not familiar with the term, parameters are the settings a large language model can tweak to provide you with an answer. The naming is slightly confusing here, but gpt-oss-120b is a 117 billion parameter model, while its smaller sibling is a 21-billion one. In practice, that means gpt-oss-120b requires more powerful hardware to run, with OpenAI recommending a single 80GB GPU for efficient use. The good news is the company says any modern computer with 16GB of RAM can run gpt-oss-20b. As a result, you could use the smaller model to do something like vibe code on your own computer without a connection to the internet. What's more, OpenAI is making the models available through the Apache 2.0 license, giving people a great deal of flexibility to modify the systems to their needs. Despite this not being a new commercial release, OpenAI says the new models are in many ways comparable to its proprietary systems. The one limitation of the oss models is that they don't offer multi-modal input, meaning they can't process images, video and voice. For those capabilities, you'll still need to turn to the cloud and OpenAI's commercial models, something both new open-weight systems can be configured to do. Beyond that, however, they offer many of the same capabilities, including chain-of-thought reasoning and tool use. That means the models can tackle more complex problems by breaking them into smaller steps, and if they need additional assistance, they know how to use the web and coding languages like Python. Additionally, OpenAI trained the models using techniques the company previously employed in the development of o3 and its other recent frontier systems. In competition-level coding gpt-oss-120b earned a score that is only a shade worse than o3, OpenAI's current state-of-the-art reasoning model, while gpt-oss-20b landed in between o3-mini and o4-mini. Of course, we'll have to wait for more real-world testing to see how the two new models compare to OpenAI's commercial offerings and those of its rivals. The release of gpt-oss-120b and gpt-oss-20b and OpenAI's apparent willingness to double down on open-weight models comes after Mark Zuckerberg signaled Meta would release fewer such systems to the public. Open-sourcing was previously central to Zuckerberg's messaging about his company's AI efforts, with the CEO once remarking about closed-source systems "fuck that." At least among the sect of tech enthusiasts willing to tinker with LLMs, the timing, accidental or not, is somewhat embarrassing for Meta. "One could argue that open-weight models democratize access to the largest, most capable models to people who don't have these massive, hyperscale data centers with lots of GPUs," said Professor Lee. "It allows people to use the outputs or products of a months-long training process on a massive data center without having to invest in that infrastructure on their own. From the perspective of someone who just wants a really capable model to begin with, and then wants to build for some application. I think open-weight models can be really useful." OpenAI is already working with a few different organizations to deploy their own versions of these models, including AI Sweden, the country's national center for applied AI. In a press briefing OpenAI held before today's announcement, the team that worked on gpt-oss-120b and gpt-oss-20b said they view the two models as an experiment; the more people use them, the more likely OpenAI is to release additional open-weight models in the future.

OpenAI releases lower-cost models to rival Meta, Mistral and DeepSeek

OpenAI on Tuesday released two open-weight language models for the first time since it rolled out GPT-2 in 2019. The text-only models are called gpt-oss-120b and gpt-oss-20b, and are designed to serve as lower-cost options that developers, researchers and companies can easily run and customize, OpenAI said. An artificial intelligence model is considered open weight if its parameters, or the elements that improve its outputs and predictions during training, are publicly available. Open-weight models can offer transparency and control, but they are different from open-source models, whose full source code becomes available for people to use and modify. Several other tech companies, including Meta, Microsoft-backed Mistral AI and the Chinese startup DeepSeek, have also released open-weight models in recent years. "It's been exciting to see an ecosystem develop, and we are excited to contribute to that and really push the frontier and then see what happens from there," OpenAI President Greg Brockman told reporters during a briefing. The company collaborated with Nvidia, Advanced Micro Devices, Cerebras, and Groq to ensure the models will work well on a variety of chips. "OpenAI showed the world what could be built on Nvidia AI -- and now they're advancing innovation in open-source software," Nvidia CEO Jensen Huang said in a statement.

OpenAI Finally Lives Up to Its Name, Drops Two New Open Source AI Models

The AI company is looking to get back in the business of transparency. For the first time in five years, OpenAI has released two new free and open-source AI models that are lightweight and designed to be easily integrated into other software programs. In a blog post on Tuesday, the company characterized gpt-oss-120b and gpt-oss-20b as flexible but powerful AI algorithms that can perform a variety of tasks and be used in numerous settings. The company also included a feedback portal and a more extensive blog that further explains the models and how they work. OpenAI's CEO, Sam Altman, said via X on Tuesday that he hoped that the AI would assist with "new kinds of research and the creation of new kinds of products." Altman also seemed to champion the open source method: "We believe in individual empowerment," he said. "Although we believe most people will want to use a convenient service like ChatGPT, people should be able to directly control and modify their own AI when they need to, and the privacy benefits are obvious." Unlike most of ChatGPT's products, open-source models disclose the training parameters that were used to build their system. This level of transparency affords onlookers the benefit of knowing how the system functions and why it might behave in the ways that it does. The last time OpenAI released an open-source model was during Trump's first presidency (truly, that feels like decades ago), with the release of GPT-2. That was when OpenAI was still a little-known startup, and it would be several years before the launch of ChatGPT in 2022. Back in those days, the company was still routinely being referred to by monikers like "Elon Musk's AI project," despite the fact that Musk had parted ways with the company. Perhaps most importantly, you won't have to pay a dime to use these models. And as long as your computer's specs are up to snuff, you can run them locally instead of relying on OpenAI's servers. Any preppers out there looking for an AI doomsday machine might want to look into it. From the looks of things, there's a lot of promising stuff in the company's new releases. gpt-oss is built to integrate into agentic workflows, OpenAI says, which means that new types of automated workâ€"conducted by so-called "agents"â€"can be powered by the new algorithms. The new models also fall under the Apache 2.0 license, which allows users to create new software with the algorithms without worrying about getting sued. "Build freely without worrying about copyleft restrictions or patent riskâ€"whether you're experimenting, customizing, or deploying commercially," OpenAI writes. The open-source ecosystem largely subsists on a plethora of such licensing agreements, allowing companies to build off free models. OpenAI also paid some lip service to AI safety in its announcement. The company claims that, in addition to "running the models through comprehensive safety training and evaluations," it also "introduced an additional layer of evaluation by testing an adversarially fine-tuned version of gpt-oss-120b" using its Preparedness Framework, which is designed to assess and track risky behavior in large language models. In recent years, much criticism has been aimed at OpenAI over its decision to continue the "walled garden" approach to software development. The company's LLM releases have stayed proprietary and, thus, shut off from public inspection. Now, OpenAI is obviously trying to prove the haters wrong and double down on its commitment to being a truly "open" organization. It's anybody's guess as to whether the company will be able to maintain such a commitment to the FOSS ethos, given that it also happens to be an organization worth hundreds of billions of dollars on paper. The organized money behind OpenAI may continue to see a benefit in owning systems that are exclusive, closed, and, most importantly, only controlled by a select group of insiders. It's noteworthy that GPT-5, the company's most powerful and highly anticipated new model, will almost certainly be released in the same fashion as other recent GPT releases: closed.

OpenAI releases two open-weight AI models, including one that runs well on Apple Silicon Macs - 9to5Mac

Living up to its name, OpenAI has released not one but two new open-weight AI models after promising to deliver a new open-weight model earlier this year. The two models, gpt-oss-20b and gpt-oss-120b, are available to download for free now. What makes open-weight models special? Specifically, these are AI models that can be downloaded and run on computers with adequate resources for powering local AI models. No internet connection is required because access to the model provider's server isn't involved. This also allows developers to build custom tools using the open AI models. OpenAI describes the '20b' variant as a medium-sized open model while it calls the '120b' variant a large-sized open model for running on "most desktops and laptops." OpenAI says the smaller model works best with at least 16GB VRAM or unified memory and is "perfect for higher-end consumer GPUs and Apple Silicon Macs." The larger, full-sized model wants at least 60GB VRAM or unified memory. While these models aren't the most powerful AI tools from OpenAI, they should satisfy the demand for open-weight models from the team behind ChatGPT. The rise of DeepSeek out of China last year put pressure on OpenAI to deliver a modern open-weight model. Prior to today, OpenAI hadn't released an open-weight model since its early days in 2019.

OpenAI releases gpt-oss, new open-weight models that can run on laptops: How to try them

For the first time since GPT-2 dropped over five years ago, OpenAI is releasing not one, but two open-weight AI reasoning models -- and they're available to download for free right now on Hugging Face. Billed as "state-of-the-art," the new OpenAI open-weight models were announced Tuesday in a company blog post. OpenAI says they "outperform similarly sized open models on reasoning, excel at tool use, and are optimized for efficient deployment on consumer hardware." This Tweet is currently unavailable. It might be loading or has been removed. There are two versions: To try out the new OpenAI models for yourself, head to the OpenAI gpt-oss page. This release is a nod to OpenAI's early roots, when the company was more publicly committed to open-sourcing its models (hence the company's name). While these aren't "fully open source" in the strictest sense -- the training data isn't included -- they are open weight, meaning the code and model parameters are available for anyone to use, tweak, and build upon. And no, models like Meta's LLaMA aren't truly open source either -- at least not by the standards of the open-source community, which requires access to training data as a baseline. Since the release of GPT-2, OpenAI has steadily shifted toward a more closed and proprietary approach to its LLM development -- until now. The recent release of open-weight models marks a notable change in direction, and it's not happening in a vacuum. With China's DeepSeek AI and other labs in the country gaining traction and achieving impressive scores on benchmark tests, the pressure has been mounting for US tech companies to stay competitive in the global AI race. In fact, just last month, the Trump administration urged American AI developers to open source more of their technology in an effort to promote innovation aligned with "American values" and maintain a strategic edge. Regardless of the motivations behind it, this move represents a significant step forward, not just for OpenAI but for the broader open AI ecosystem.

OpenAI just announced something big, but it's not GPT-5

These models let users see each step of the AI's thinking process, making them more transparent in how they function OpenAI has just dropped two new AI models, gpt‑oss‑120b and gpt‑oss‑20b. Not only are they new, but they're the first open‑weight models from ChatGPT's creator since GPT‑2. The smaller of the two - gpt-oss-20b - is especially notable for being light enough to run on a decently specced consumer PC. If you've got about 16GB of RAM and some patience, you can load it up, ask it questions, and actually see how it arrives at answers. The larger 120b model still requires serious hardware or cloud support. Both models are part of OpenAI's new push to encourage developers to play around with the models and even commercialize them for the average user. For the first time in years, developers and curious individuals alike can download and run OpenAI models on their own machines, inspect how they think, and build on them freely. They're available via Hugging Face and AWS under the Apache 2.0 license. Being open weight means the models provide a level of transparency and independence that most people haven't had since ChatGPT first went viral. The real-time reasoning is visible throughout, and you can see how the 'logic' of the model leads to its final options for how to respond and how it makes that decision. That's a big shift for OpenAI. The company has spent several years restricting access to its most powerful tools, offering only API endpoints and paid tiers. Now it's returning a little bit to the GPT-2 era, but with far more capable models. Even so, the lighter model isn't something everyone will rush to as a replacement for the ChatGPT app. The flexibility provided by the new models could be a boon for OpenAI as the open-weight approach becomes more popular. DeepSeek, Meta, and Mistral have all released open models in some fashion recently. But most have turned out to be semi-open, meaning they are trained on undisclosed data or have constricted terms and usage limits. The gpt-oss models are straightforward in offering the weights and license, though the training data remains proprietary. And OpenAI's gpt-oss models bring compatibility with OpenAI's widely used interface, as well as a bigger window into how the model makes decisions that stand out. So, where DeepSeek models tend to emphasize the raw power and relatively low-cost performance of their models, OpenAI is more interested in explaining how the models work. That will pique the interest of many developers trying to learn or experiment. You can literally pause and look at what's going on inside the model. It's also a signal to the developer community: OpenAI is inviting people to build with its tech in a way that hasn't been possible for years. These models don't just output chat completions; they offer a foundation. With the Apache license, you can fine-tune, distill, embed, or wrap them into entirely new products. And you don't need to send data to a third party to do it. Those hoping for a more decentralized AI ecosystem are likely to be pleased. For those of us who are less technical, the main point is that you might see AI apps that run very well without needing a subscription price from you, or personalized tools that don't send your data to the cloud. With this release, OpenAI can claim to be willing to share more with developers, if not quite everything. For a company that's spent much of the past two years building closed systems and premium subscriptions, releasing a pair of models this capable under open licenses feels like a major philosophical change, or at least the desire to make such a change seem real. And while these two models don't represent the push into the era of GPT-5, OpenAI chief Sam Altman has teased that even more announcements are on the horizon, so it's likely that the next-generation model is still being readied.

OpenAI returns to open source roots with new models gpt-oss-120b and gpt-oss-20b

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now OpenAI is getting back to its roots as an open source AI company with today's announcement and release of two new, open source, frontier large language models (LLMs): gpt-oss-120b and gpt-oss-20b. The former is a 120-billion parameter model as the name would suggest, capable of running on a single Nvidia H100 graphics processing unit (GPU) and the latter is only 20 billion, small enough to run locally on a consumer laptop or desktop PC. Both are text-only language models, which means unlike the multimodal AI that we've had for nearly two years that allows users to upload files and images and have the AI analyze them, users will be confined to only inputting text messages to the models and receiving text back out. However, they can still of course write code and provide math problems and numerics, and in terms of their performance on tasks, they rank above some of OpenAI's paid models and much of the competition globally. They can also be connected to external tools including web search to perform research on behalf of the user. More on this below. Most importantly: they're free, they're available for enterprises and indie developers to download the code and use right now, modifying according to their needs, and can be run locally without a web connection, ensuring maximum privacy, unlike the other top OpenAI models and those from leading U.S.-based rivals Google and Anthropic. The models can be downloaded today with full weights (the settings guiding its behavior) on the AI code sharing community Hugging Face and GitHub. High benchmark scores According to OpenAI, gpt-oss-120b matches or exceeds its proprietary o4-mini model on reasoning and tool-use benchmarks, including competition mathematics (AIME 2024 & 2025), general problem solving (MMLU and HLE), agentic evaluations (TauBench), and health-specific evaluations (HealthBench). The smaller gpt-oss-20b model is comparable to o3-mini and even surpasses it in some benchmarks. The models are multilingual and perform well across a variety of non-English languages, though OpenAI declined to specify which and how many. While these capabilities are available out of the box, OpenAI notes that localized fine-tuning -- such as an ongoing collaboration with the Swedish government to produce a version fine-tuned on the country's language -- can still meaningfully enhance performance for specific regional or linguistic contexts. A hugely advantageous license for enterprises and privacy-minded users But the biggest feature is the licensing terms for both: Apache 2.0, the same as the wave of Chinese open source models that have been released over the last several weeks, and a more enterprise-friendly license than Meta's trickier and more nuanced open-ish Llama license, which requires that users who operate a service with more than 700 million monthly active users obtain a paid license to keep using the company's family of LLMs. By contrast, OpenAI's new gpt-oss series of models offer no such restrictions. In keeping with Chinese competitors and counterparts, any consumer, developer, independent entrepreneur or enterprise large and small is empowered by the Apache 2.0 license to be able to download the new gpt-oss models at will, fine-tune and alter them to fit their specific needs, and use them to generate revenue or operate paid services, all without paying OpenAI a dime (or anything!). This also means enterprises can use a powerful, near topline OpenAI model on their own hardware totally privately and securely, without sending any data up to the cloud, on web servers, or anywhere else. For highly regulated industries like finance, healthcare, and legal services, not to mention organizations in military, intelligence, and government, this may be a requirement. Before today, anyone using ChatGPT or its application programming interface (API) -- the service that acts like a switching board and allows third-party software developers to connect their own apps and services to these OpenAI's proprietary/paid models like GPT-4o and o3 -- was sending data up to OpenAI servers that could technically be subpoenaed by government agencies and accessed without a user's knowledge. That's still the case for anyone using ChatGPT or the API going forward, as OpenAI co-founder and Sam Altman recently warned. And while running the new gpt-oss models locally on a user's own hardware disconnected from the web would allow for maximum privacy, as soon as the user decides to connect it to external web search or other web enabled tools, some of the same privacy risks and issues would then arise -- through any third-party web services the user or developer was relying on when hooking the models up to said tools. The last OpenAI open source language model was released more than six years ago "This is the first time we're releasing an open-weight language model in a long time... We view this as complementary to our other products," said OpenAI co-founder and president Greg Brockman on an embargoed press video call with VentureBeat and other journalists last night. The last time OpenAI released a fully open source language model was GPT-2 in 2019, more than six years ago, and three years before the release of ChatGPT. This fact has sparked the ire of -- and resulted in several lawsuits from -- former OpenAI co-founder and backer turned rival Elon Musk, who, along with many other critics, have spent the last several years accusing OpenAI of betraying its mission and founding principles and namesake by eschewing open source AI releases in favor of paid proprietary models available only to customers of OpenAI's API or paying ChatGPT subscribers (though there is a free tier for the latter). OpenAI co-founder CEO Sam Altman did express regret about being on the "wrong side of history" but not releasing more open source AI sooner in a Reddit AMA (ask me anything) QA with users in February of this year, and Altman committed to releasing a new open source model back in March, but ultimately the company delayed its release from a planned July date until now. Now OpenAI is tacking back toward open source, and the question is, why? Why would OpenAI release a set of free open source models that it makes no money from? To paraphrase Jesse Plemons' character's memorable line from the film Game Night: "How can that be profitable for OpenAI?" After all, business to OpenAI's paid offerings appears to be booming. Revenue has skyrocketed alongside the rapid expansion of its ChatGPT user base, now at 700 million weekly active users. As of August 2025, OpenAI reported $13 billion in annual recurring revenue, up from $10 billion in June. That growth is driven by a sharp rise in paying business customers -- now 5 million, up from 3 million just two months earlier -- and surging daily engagement, with over 3 billion user messages sent every day. The financial momentum follows an $8.3 billion funding round that valued OpenAI at $300 billion and provides the foundation for the company's aggressive infrastructure expansion and global ambitions. Compare that to closed/proprietary rival AI startup Anthropic's reported $5 billion in total annual recurring revenue, but interestingly, Anthropic is said to be getting more money from its API, $3.1 billion in revenue compared to OpenAI's $2.9 billion, according to The Information. So, given how well the paid AI business is already doing, the business strategy behind these open source offerings is less clear -- especially since the new OpenAI gpt-oss models will almost certainly cut into some (perhaps a lot of) usage of OpenAI's paid models. Why go back to offering open source LLMs now when so much money is flowing into paid and none will, by virtue of its very intent, go directly toward open source models? Put simply: because open source competitors, beginning with the release of the impressively efficient DeepSeek R1 by the Chinese AI division of the same name in January 2025, are offering near parity on performance benchmarks to paid proprietary models, for free, with fewer (basically zero) implementation restrictions for enterprises and end users. And increasingly, enterprises are adopting these open source models in production. As OpenAI executives and team members revealed to VentureBeat and many other journalists on an embargoed video call last night about the new models that when it comes to OpenAI's API, the majority of customers are using a mix of paid OpenAI models and open source models from other providers. (I asked, but OpenAI declined to specify what percentage or total number of API customers are using open source models and which ones). At least, until now. OpenAI clearly hopes these new gpt-oss offerings will get more of these users to switch away from competing open source offerings and back into OpenAI's ecosystem, even if OpenAI doesn't see any direct revenue or data from that usage. On a grander scale, it seems OpenAI wants to be a full-service, full-stack, one-stop shop AI offering for all of an enterprise, indie developer's, or regular consumer's machine intelligence needs -- from a clean chatbot interface to an API to build services and apps atop of to agent frameworks for building AI agents through said API to an image generation model (gpt-4o native image generation), video model (Sora), audio transcription model (gpt-4o-transcribe), and now, open source offerings as well. Can a music generation and "world model" be far behind? OpenAI seeks to span the AI market, propriety and open source alike, even if the latter is worth nothing in terms of actual, direct dollars and cents. Training and architecture Feedback from developers directly influenced gpt-oss's design. OpenAI says the top request was for a permissive license, which led to the adoption of Apache 2.0 for both models. Both models use a Mixture-of-Experts (MoE) architecture with a Transformer backbone. The larger gpt-oss-120b activates 5.1 billion parameters per token (out of 117 billion total), and gpt-oss-20b activates 3.6 billion (out of 21 billion total). Both support 128,000 token context length (about 300-400 pages of a novel's worth of text a user can upload at once), and employ locally banded sparse attention and use Rotary Positional Embeddings for encoding. The tokenizer -- the program that converts words and chunks of words into the numerical tokens that the LLMs can understand, dubbed "o200k_harmony" -- is also being open-sourced. Developers can select among low, medium, or high reasoning effort settings based on latency and performance needs. While these models can reason across complex agentic tasks, OpenAI emphasizes they were not trained with direct supervision of CoT outputs, to preserve the observability of reasoning behavior -- an approach OpenAI considers important for safety monitoring. Another common request from OpenAI's developer community was for strong support for function calling, particularly for agentic workloads, which OpenAI believes gpt-oss now delivers. The models are engineered for chain-of-thought reasoning, tool use, and few-shot function calling, and are compatible with OpenAI's Responses API introduced back in March, which allows developers to augment their apps by connecting an OpenAI LLM of their choice to three powerful built-in tools -- web search, file search, and computer use -- within a single API call. But for the new gpt-oss models, tool use capabilities -- including web search and code execution -- are not tied to OpenAI infrastructure. OpenAI provides the schemas and examples used during training, such as a basic browser implementation using the Exa API and a Python interpreter that operates in a Docker container. It is up to individual inference providers or developers to define how tools are implemented. Providers like vLLM, for instance, allow users to configure their own MCP (Model-Controller-Proxy) server to specify the browser backend. While these models can reason across complex agentic tasks, OpenAI emphasizes they were not trained with direct supervision of CoT outputs, to preserve the observability of reasoning behavior -- an approach OpenAI considers important for safety monitoring. Safety evaluations and measures OpenAI conducted safety training using its Preparedness Framework, a document that outlines the procedural commitments, risk‑assessment criteria, capability categories, thresholds, evaluations, and governance mechanisms OpenAI uses to monitor, evaluate, and mitigate frontier AI risks. These included filtering chemical, biological, radiological, and nuclear threat (CBRN) related data out during pretraining, and applying advanced post-training safety methods such as deliberative alignment and an instruction hierarchy to enforce refusal behavior on harmful prompts. To test worst-case misuse potential, OpenAI adversarially fine-tuned gpt-oss-120b on sensitive biology and cybersecurity data using its internal RL training stack. These malicious fine-tuning (MFT) scenarios -- one of the most sophisticated evaluations of this kind to date -- included enabling browsing and disabling refusal behavior, simulating real-world attack potential. The resulting models were benchmarked against both open and proprietary LLMs, including DeepSeek R1-0528, Qwen 3 Thinking, Kimi K2, and OpenAI's o3. Despite enhanced access to tools and targeted training, OpenAI found that even the fine-tuned gpt-oss models remained below the "High" capability threshold for frontier risk domains such as biorisk and cybersecurity. These conclusions were reviewed by three independent expert groups, whose recommendations were incorporated into the final methodology. In parallel, OpenAI partnered with SecureBio to run external evaluations on biology-focused benchmarks like Human Pathogen Capabilities Test (HPCT), Molecular Biology Capabilities Test (MBCT), and others. Results showed that gpt-oss's fine-tuned models performed close to OpenAI's o3 model, which is not classified as frontier-high under OpenAI's safety definitions. According to OpenAI, these findings contributed directly to the decision to release gpt-oss openly. The release is also intended to support safety research, especially around monitoring and controlling open-weight models in complex domains. Availability and ecosystem support The gpt-oss models are now available on Hugging Face, with pre-built support through major deployment platforms including Azure, AWS, Databricks, Cloudflare, Vercel, Together AI, OpenRouter, and others. Hardware partners include NVIDIA, AMD, and Cerebras, and Microsoft is making GPU-optimized builds available on Windows via ONNX Runtime. OpenAI has also announced a $500,000 Red Teaming Challenge hosted on Kaggle, inviting researchers and developers to probe the limits of gpt-oss and identify novel misuse pathways. A public report and an open-source evaluation dataset will follow, aiming to accelerate open model safety research across the AI community. Early adopters such as AI Sweden, Orange, and Snowflake have collaborated with OpenAI to explore deployments ranging from localized fine-tuning to secure on-premise use cases. OpenAI characterizes the launch as an invitation for developers, enterprises, and governments to run state-of-the-art language models on their own terms. While OpenAI has not committed to a fixed cadence for future open-weight releases, it signals that gpt-oss represents a strategic expansion of its approach -- balancing openness with aligned safety methodologies to shape how large models are shared and governed in the years ahead. The big question: with so much competition in open source AI, will OpenAI's own efforts pay off? OpenAI re-enters the open source model market in the most competitive moment yet. At the top of public AI benchmarking leaderboards, U.S. frontier models remain proprietary -- OpenAI (GPT-4o/o3), Google (Gemini), and Anthropic (Claude). But they now compete directly with a surge of open-weights contenders. From China: DeepSeek-R1 (open source, MIT) and DeepSeek-V3 (open-weights under a DeepSeek Model License that permits commercial use); Alibaba's Qwen 3 (open-weights, Apache-2.0); MoonshotAI's Kimi K2 (open-weights; public repo and model cards); and Z.ai's GLM-4.5 (also Apache 2.0 licensed). Europe's Mistral (Mixtral/Mistral, open-weights, Apache-2.0) anchors the EU push; the UAE's Falcon 2/3 publish open-weights under TII's Apache-based license. In the U.S. open-weights camp, Meta's Llama 3.1 ships under a community (source-available) license, Google's Gemma under Gemma terms (open weights with use restrictions), and Microsoft's Phi-3.5 under MIT. Developer pull mirrors that split. On Hugging Face, Qwen2.5-7B-Instruct (open-weights, Apache-2.0) sits near the top by "downloads last month," while DeepSeek-R1 (MIT) and DeepSeek-V3 (model-licensed open weights) also post heavy traction. Open-weights stalwarts Mistral-7B / Mixtral (Apache-2.0), Llama-3.1-8B/70B (Meta community license), Gemma-2 (Gemma terms), Phi-3.5 (MIT), GLM-4.5 (open-weights), and Falcon-2-11B (TII Falcon License 2.0) round out the most-pulled families -- underscoring that the open ecosystem spans the U.S., Europe, the Middle East, and China. Hugging Face signals adoption, not market share, but they show where builders are experimenting and deploying today. Consumer usage remains concentrated in proprietary apps even as weights open up. ChatGPT still drives the largest engagement globally (about 2.5 billion prompts/day, proprietary service), while in China the leading assistants -- ByteDance's Doubao, DeepSeek's app, Moonshot's Kimi, and Baidu's ERNIE Bot -- are delivered as proprietary products, even as several base models (GLM-4.5, ERNIE 4.5 variants) now ship as open-weights. But now that a range of powerful open source models are available to businesses and consumers -- all nearing one another in terms of performance -- and can be downloaded on consumer hardware, the big question facing OpenAI is: who will pay for intelligence at all? Will the convenience of the web-based chatbot interface, multimodal capabilities, and more powerful performance be enough to keep the dollars flowing? Or has machine intelligence already become, in the words of Atlman himself, "too cheap to meter"? And if so, how to build a successful business atop it, especially with OpenAI and other AI firms' sky-high valuations and expenditures. One clue: OpenAI is already said to be offering in-house engineers to help its enterprise customers customize and deploy fine-tuned models, similar to Palantir's "forward deployed" software engineers (SWEs), essentially charging for experts to come in, set up the models correctly, and train employees how to use them for best results. Perhaps the world will migrate toward a majority of AI usage going to open source models, or a sizeable minority, with OpenAI and other AI model providers offering experts to help install said models into enterprises. Is that enough of a service to build a multi-billion dollar business upon? Or will enough people continue paying $20, $200 or more each month to have access to even more powerful proprietary models? I don't envy the folks at OpenAI figuring out all the business calculations -- despite what I assume to be hefty compensation as a result, at least for now. But for end users and enterprises, the release of the gpt-oss series is undoubtedly compelling.

OpenAI releases powerful new open language models

Why it matters: OpenAI is aiming the new models at customers who want the cost savings and privacy benefits that come from running AI models directly on their own devices rather than relying on cloud-based services like ChatGPT or its rivals. * It's also pitching the open models for countries that want to avoid getting their AI tools from the cloud servers of Google, Microsoft or other tech giants. The big picture: The arrival of China's DeepSeek earlier this year jolted the open-model world and suggested that China might be taking the lead in that category, while Meta's commitment to its open source Llama project has come into question as the company pivots to the "superintelligence" race. What they're saying: "We're excited to make this model, the result of billions of dollars of research, available to the world to get AI into the hands of the most people possible," CEO Sam Altman said in a statement. * "Going back to when we started in 2015, OpenAI's mission is to ensure AGI that benefits all of humanity," Altman said. "To that end, we are excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit." Driving the news: OpenAI is releasing two new open models, both capable of chain-of-thought reasoning and accessing the web. They can also, if desired, work in conjunction with larger cloud-based AI models. * The first, a 117 billion parameter model called gpt-oss-120b, can run on a single GPU with 80 gigabytes of RAM. * The second, with 21 billion parameters called gpt-oss-20b, is designed to run on laptops or other devices with 16 gigabytes of RAM. * Both models are available via Hugging Face and other cloud providers. Microsoft is also making available a version of the smaller model that has been optimized to run on Windows devices. * The company provided various benchmarks showing the open models performing at or near the performance of the company's o3 and o4-mini models. Yes, but: The new open-models are text-only, as compared to most of OpenAI's recent models, which are so-called multimodal models capable of processing and outputting text, images, audio and video. Between the lines: Technically, the models are "open weights" versus "open source," meaning anyone can download and fine-tune the models but there's no public access to other key information, like training data details.

The initial reactions to OpenAI's landmark open source gpt-oss models are highly varied and mixed

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now OpenAI's long-awaited return to the "open" of its namesake occurred yesterday with the release of two new large language models (LLMs): gpt-oss-120B and gpt-oss-20B. But despite achieving technical benchmarks on par with OpenAI's other powerful proprietary AI model offerings, the broader AI developer and user community's initial response has so far been all over the map. If this release were a movie premiering and being graded on Rotten Tomatoes, we'd be looking at a near 50% split, based on my observations. First some background: OpenAI has released these two new text-only language models (no image generation or analysis) both under the permissive open source Apache 2.0 license -- the first time since 2019 (before ChatGPT) that the company has done so with a cutting-edge language model. The entire ChatGPT era of the last 2.7 years has so far been powered by proprietary or closed-source models, ones that OpenAI controlled and that users had to pay to access (or use a free tier subject to limits), with limited customizability and no way to run them offline or on private computing hardware. But that all changed thanks to the release of the pair of gpt-oss models yesterday, one larger and more powerful for use on a single Nvidia H100 GPU at say, a small or medium-sized enterprise's data center or server farm, and an even smaller one that works on a single consumer laptop or desktop PC like the kind in your home office. Of course, the models being so new, it's taken several hours for the AI power user community to independently run and test them out on their own individual benchmarks (measurements) and tasks. And now we're getting a wave of feedback ranging from optimistic enthusiasm about the potential of these powerful, free, and efficient new models to an undercurrent of dissatisfaction and dismay with what some users see as significant problems and limitations, especially compared to the wave of similarly Apache 2.0-licensed powerful open source, multimodal LLMs from Chinese startups (which can also be taken, customized, run locally on U.S. hardware for free by U.S. companies, or companies anywhere else around the world). High benchmarks, but still behind Chinese open source leaders Intelligence benchmarks place the gpt-oss models ahead of most American open-source offerings. According to independent third-party AI benchmarking firm Artificial Analysis, gpt-oss-120B is "the most intelligent American open weights model," though it still falls short of Chinese heavyweights like DeepSeek R1 and Qwen3 235B. "On reflection, that's all they did. Mogged on benchmarks," wrote self-proclaimed DeepSeek "stan" @teortaxesTex. "No good derivative models will be trained... No new usecases created... Barren claim to bragging rights." That skepticism is echoed by pseudonymous open source AI researcher Teknium (@Teknium1), co-founder of rival open source AI model provider Nous Research, who called the release "a legitimate nothing burger," on X, and predicted a Chinese model will soon eclipse it. "Overall very disappointed and I legitimately came open minded to this," they wrote. Bench-maxxing on math and coding at the expense of writing? Other criticism focused on the gpt-oss models' apparent narrow usefulness. AI influencer "Lisan al Gaib (@scaling01)" noted that the models excel at math and coding but "completely lack taste and common sense." He added, "So it's just a math model?" In creative writing tests, some users found the model injecting equations into poetic outputs. "This is what happens when you benchmarkmax," Teknium remarked, sharing a screenshot where the model added an integral formula mid-poem. And @kalomaze, a researcher at decentralized AI model training company Prime Intellect, wrote that "gpt-oss-120b knows less about the world than what a good 32b does. probably wanted to avoid copyright issues so they likely pretrained on majority synth. pretty devastating stuff" Former Googler and independent AI developer Kyle Corbitt agreed that the gpt-oss pair of models seemed to have been trained primarily on synthetic data -- that is, data generated by an AI model specifically for the purposes of training a new one -- making it "extremely spiky." It's "great at the tasks it's trained on, really bad at everything else," Corbitt wrote, i.e., great on coding and math problems, and bad at more linguistic tasks like creative writing or report generation. In other words, the charge is that OpenAI deliberately trained the model on more synthetic data than real world facts and figures to avoid using copyrighted data scraped from websites and other repositories it doesn't own or have license to use, which is something it and many other leading gen AI companies have been accused of in the past and are facing down ongoing lawsuits as a result of. Others speculated OpenAI may have trained the model on primarily synthetic data to avoid safety and security issues, resulting in worse quality than if it had been trained on more real world (and presumably copyrighted) data. Concerning third-party benchmark results Moreover, evaluating the models on third-party benchmarking tests have turned up concerning metrics in some users' eyes. SpeechMap -- which measures the performance of LLMs in complying with user prompts to generate disallowed, biased, or politically sensitive outputs -- showed compliance scores for gpt-oss 120B hovering under 40%, near the bottom of peer open models, which indicates resistance to follow user requests and defaulting to guardrails, potentially at the expense of providing accurate information. In Aider's Polyglot evaluation, gpt-oss-120B scored just 41.8% in multilingual reasoning -- far below competitors like Kimi-K2 (59.1%) and DeepSeek-R1 (56.9%). Some users also said their tests indicated the model is oddly resistant to generating criticism of China or Russia, a contrast to its treatment of the US and EU, raising questions about bias and training data filtering. Other experts have applauded the release and what it signals for U.S. open source AI To be fair, not all the commentary is negative. Software engineer and close AI watcher Simon Willison called the release "really impressive" on X, elaborating in a blog post on the models' efficiency and ability to achieve parity with OpenAI's proprietary o3-mini and o4-mini models. He praised their strong performance on reasoning and STEM-heavy benchmarks, and hailed the new "Harmony" prompt template format -- which offers developers more structured terms for guiding model responses -- and support for third-party tool use as meaningful contributions. In a lengthy X post, Clem Delangue, CEO and co-founder of AI code sharing and open source community Hugging Face, encouraged users not to rush to judgment, pointing out that inference for these models is complex, and early issues could be due to infrastructure instability and insufficient optimization among hosting providers. "The power of open-source is that there's no cheating," Delangue wrote. "We'll uncover all the strengths and limitations... progressively." Even more cautious was Wharton School of Business at the University of Pennsylvania professor Ethan Mollick, who wrote on X that "The US now likely has the leading open weights models (or close to it)", but questioned whether this is a one-off by OpenAI. "The lead will evaporate quickly as others catch up," he noted, adding that it's unclear what incentives OpenAI has to keep the models updated. Nathan Lambert, a leading AI researcher at the rival open source lab Allen Institute for AI (Ai2) and commentator, praised the symbolic significance of the release on his blog Interconnects, calling it "a phenomenal step for the open ecosystem, especially for the West and its allies, that the most known brand in the AI space has returned to openly releasing models." But he cautioned on X that gpt-oss is "unlikely to meaningfully slow down [Chinese e-commerce giant Aliaba's AI team] Qwen," citing its usability, performance, and variety. He argued the release marks an important shift in the U.S. toward open models, but that OpenAI still has a "long path back" to catch up in practice. A split verdict The verdict, for now, is split. OpenAI's gpt-oss models are a landmark in terms of licensing and accessibility. But while the benchmarks look solid, the real-world "vibes" -- as many users describe it -- are proving less compelling. Whether developers can build strong applications and derivatives on top of gpt-oss will determine whether the release is remembered as a breakthrough or a blip.

AI execs back OpenAI's open-source return

State of play: OpenAI's new models are designed for customers who want the cost savings and privacy that come from running AI models directly on their own devices rather than relying on cloud-based services like ChatGPT or its rivals. * The company is also pitching the models to countries seeking greater control, local data storage and independence from cloud providers like Google and Microsoft. What they're saying: OpenAI CEO Sam Altman stressed the political importance of the release and industry leaders were quick to echo his sentiments. * "We are excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit," Altman said in a statement. * Box CEO Aaron Levie warned that if U.S. companies fall behind, Chinese firms could dominate open-source models optimized for Huawei chips. "It's important that America stays in the game," Levie said. "And it's great that OpenAI is taking the lead on that." * Hugging Face CEO Clement Delangue called the release "critically important," pointing to Trump's AI Action Plan's call for stronger American open source AI foundations. * Developer Simon Willison called the models "very impressive" and "eyebrow-raising," noting he hadn't expected open-weight models of this size to perform so well. For Amazon and cloud providers beyond Microsoft, the move lets them offer access to OpenAI models for the first time. * "It does look like a very impressive model that competes very, very well with everything out there," AWS VP David Brown told Axios. "It's very similar to the o4-mini model, which is a very capable model." * While Amazon will charge customers for the computing cost of serving up the new models (as it does with Llama and other open source models), Brown said Amazon expects the new OpenAI models to offer twice as much performance for the price as OpenAI's comparable o4 models, three times as much performance for the price of a comparable Google Gemini model and five times that of DeepSeek. * "This is definitely going to be something that I think customers are going to have a very keen interest in," he said. Driving the news: Both of OpenAI's new models are capable of chain-of-thought reasoning and accessing the web. * The first, a 117 billion parameter model called gpt-oss-120b, can run on a single GPU with 80 gigabytes of RAM. * The second, with 21 billion parameters called gpt-oss-20b, is designed to run on laptops or other devices with 16 gigabytes of RAM. * Both models are available via the open source hosting platform Hugging Face and cloud providers, including Amazon. Microsoft is also offering a version of the smaller model that has been optimized to run on Windows devices. * The company provided various benchmarks showing the open models performing at or near the performance of the company's o3 and o4-mini models. Yes, but: The new open models are text-only, as compared to most of OpenAI's recent models, which are capable of processing and outputting text, images, audio and video. * OpenAI wouldn't commit to a specific schedule for future open models. OpenAI hasn't released an open large language model since GPT-2 in 2019. Between the lines: Technically, the models are "open weights" versus "open source," meaning anyone can download and fine-tune the models, but there's no public access to other key information, like training data details. * That's similar to DeepSeek and many of Meta's Llama models, but not as open as OLMo from the Allen Institute for AI. * OpenAI declined to comment to Axios on what the new models were trained on or how the training may differ from that of its closed models. What to watch: Although the new models are competitive with current OpenAI models, the company's newest model -- GPT-5 -- is rumored to arrive in the coming days.

OpenAI releases free, downloadable models in competition catch-up

OpenAI on Tuesday released two new artificial intelligence (AI) models that can be downloaded for free and altered by users, to challenge similar offerings by US and Chinese competition. The release of gpt-oss-120b and gpt-oss-20b "open-weight language models" comes as the ChatGPT-maker is under pressure to share inner workings of its software in the spirit of its origin as a nonprofit. "Going back to when we started in 2015, OpenAI's mission is to ensure AGI (Artificial General Intelligence) that benefits all of humanity," said OpenAI chief executive Sam Altman. An open-weight model, in the context of generative AI, is one in which the trained parameters are made public, enabling users to fine-tune it. Meta touts its open-source approach to AI, and Chinese AI startup DeepSeek rattled the industry with its low-cost, high-performance model boasting an open weight approach that allows users to customize the technology. "This is the first time that we're releasing an open-weight model in language in a long time, and it's really incredible," OpenAI co-founder and president Greg Brockman said during a briefing with journalists. The new, text-only models deliver strong performance at low cost, according to OpenAI, which said they are suited for AI jobs like searching the internet or executing computer code, and are designed to be easy to run on local computer systems. "We are quite hopeful that this release will enable new kinds of research and the creation of new kinds of products," Altman said. OpenAI said it is working with partners including French telecommunications giant Orange and cloud-based data platform Snowflake on real-world uses of the models. The open-weight models have been tuned to thwart being used for malicious purposes, according to OpenAI. Altman early this year said his company had been "on the wrong side of history" when it came to being open about how its technology works. He later announced that OpenAI will continue to be run as a nonprofit, abandoning a contested plan to convert into a for-profit organization. The structural issue had become a point of contention, with major investors pushing for better returns. That plan faced strong criticism from AI safety activists and co-founder Elon Musk, who sued the company he left in 2018, claiming the proposal violated its founding philosophy. In the revised plan, OpenAI's money-making arm will be open to generate profits but will remain under the nonprofit board's supervision.

OpenAI's open source pivot shows how U.S. tech is trying to catch up to China's AI surge

OpenAI, the developer behind ChatGPT, released two bombshell AI developments last week. Last Thursday, it released GPT-5, the long-awaited update to its powerful GPT model. But OpenAI's earlier decision to release open-source versions of its powerful model -- the first time it's done so since 2020, may be more consequential. OpenAI's move follows a flood of Chinese AI models spurred by the surprise release from Chinese AI startup DeepSeek. It's a major shift for the U.S. AI developer, now worth $300 billion. Open weight models allow developers to fine-tune for specific tasks without retraining it from scratch. Despite its name, OpenAI has focused on releasing closed, proprietary models, meaning developers couldn't get under the hood to see how they worked -- allowing OpenAI to charge for access to its powerful models. DeepSeek tested that strategy. The Hangzhou-based start-up made waves by releasing models that matched the performance of products from Western rivals like OpenAI and Anthropic. By making its technology openly accessible, DeepSeek allowed developers around the globe to experience the power of its models firsthand. Since then, Chinese AI development has exploded, with companies large and small rushing to unveil increasingly advanced models. Most releases are open-source. "Globally, AI labs are feeling the heat as open source models are increasingly recognized for their role in democratizing AI development," Grace Shao, an China-based AI analyst and founder of AI Proem, says. U.S. tech stocks have rebounded from the slump triggered by DeepSeek, but the shift to open-source may be more permanent. In March, OpenAI CEO Sam Altman conceded that the developer may have been on the "wrong side of history" by maintaining a closed approach. The race is now geopolitically charged. Ahead of releasing the open-source models, Altman said he was "excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit." Altman's statement leans into a growing competition over AI-one that developers in the U.S. are worried of losing. "This plethora of simultaneous open AI models (with published weights and papers about technique) is an 'idea orgy.' The collective innovation should easily soar past anything one company can do alone," Benchmark general partner Bill Gurley wrote on X in late July. "It's formidable and should easily win over single proprietary players (anywhere in the globe)." Chinese AI firms are now aggressively championing open-source. Baidu, once the leader in China's AI development with its ERNIE model, went open-source a few months ago to catch up with Alibaba and DeepSeek. Kuaishou and Tencent have both released open-source video-generation models. Zhipu AI, Moonshot AI and MiniMax-some of China's so-called "AI tigers" -- have also released open-source models in recent weeks. Rather than closely guard their breakthroughs, Chinese developers think an open approach will encourage greater innovation and encourage adoption. "When the model is open-source, people naturally want to try it out of curiosity," Baidu CEO Robin Li told analysts in February, soon after the company unveiled its plans to go open-source And there's a business argument too: Alibaba executives, for example, argue that their open-source Qwen models encourage companies and startups to use Alibaba's cloud computing services. Since DeepSeek's release, Chinese companies have rushed to integrate Chinese AI models into their products, including social media platforms, cars, and even air-conditioners. There may also be a psychological element at play. Going open-source lets users around the world see the power of Chinese AI models for themselves, appealing to an up-and-coming tech sector that's long been denigrated by outsiders as a copycat. China has supported other open-source technologies. Officials back the use of the RISC-V chip design architecture, an open-source alternative to proprietary architectures like ARM and Intel's x86. RISC-V allows Chinese chip engineers to share best practices and ideas, spurring the growth of the broader sector. Beijing seeks to develop a self-sufficient semiconductor sector, in part due to concerns of the U.S.'s control of critical parts of the chip supply chain. The Biden administration's decision to impose chip controls in 2022 intensified China's push for domestic innovation. China's embrace of RISC-V has raised eyebrows in Washington. Last year, the House Select Committee on the Chinese Communist Party recommended that U.S. officials study the risks of RISC-V, and reportedly proposed preventing U.S. citizens from aiding China on the open-source architecture. China's embrace of open-source aligns with the country's initial position as a runner-up in AI. "If you're an OpenAI, an Anthropic, a Google...if you're really leading, then you have this incredibly valuable asset," Helen Toner, the director of strategy at Georgetown's Center for Security and Emerging Technology, said at the Fortune Brainstorm AI Singapore conference in mid-July. "It's easy to understand why they wouldn't want to just hand out [their models] for free to their competitors if they're able to sell access to their closed systems at a premium." But for followers, who "can't compete at the frontier," releasing an open-source model is a way to show "how advanced you are," she explained. Open-source models also "buy a lot of goodwill," Toner, who once served on OpenAI's board, added. "What we've seen over the last couple years is how much soft power is available to people who are willing to and organizations that are willing to make their technology available freely," she explained. The U.S. may now recognize the "soft power" potential of open-source. "The United States is committed to supporting the development and deployment of open-source and open-weight models," Michael Kratsios, director of the U.S. Office of Science and Technology Policy, said in South Korea earlier this week And with OpenAI's decision, U.S. AI is now perhaps put in a rare position: Following, not leading.

OpenAI's new 'open' models are here -- just don't expect them to reveal the secret recipe

Despite what its name suggests, OpenAI hadn't released an "open" model -- one that includes access to the weights, or the numerical parameters often described as the model's brains -- since GPT-2 in 2020. That changed on Tuesday: The company launched a long-awaited open-weight model, in two sizes, that the company says pushes the frontier of reasoning in open-source AI. "We're excited to make this model, the result of billions of dollars of research, available to the world to get AI into the hands of the most people possible," said OpenAI CEO Sam Altman about the release. "As part of this, we are quite hopeful that this release will enable new kinds of research and the creation of new kinds of products." He emphasized that he is "excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit." OpenAI CEO Sam Altman had teased the upcoming models back in March, two months after admitting, in the wake of the success of China's open models from DeepSeek, that the company had been "on the wrong side of history" when it came to opening up its models to developers and builders. But while the weights are now public, experts note that OpenAI's new models are hardly "open." By no means is it giving away its crown jewels: The proprietary architecture, routing mechanisms, training data and methods that power its most advanced models -- including the long-awaited GPT-5, widely expected to be released sometime this month -- remain tightly under wraps. The two new model names - gpt-oss-120b and gpt-oss-20b - may be indecipherable to non-engineers, but that's because OpenAI is setting its sights on AI builders and developers seeking to rapidly build on real-world use cases on their own systems. The company noted that the larger of the two models can run on a single Nvidia 80GB chip while the smaller one fits on consumer hardware like a Mac laptop. Greg Brockman, co-founder and president of OpenAI, acknowledged on a press pre-briefing call that "it's been a long time" since the company had released an open model. He added that it is "something that we view as complementary to the other products that we release" and along with OpenAI's proprietary models, "combine to really accelerate our mission of ensuring that API benefits all of humanity." OpenAI said the new models perform well on reasoning benchmarks, which have emerged as the key measurements for AI performance, with models from OpenAI, Anthropic, Google and DeepSeek fiercely competing over their abilities to tackle multi-step logic, code generation, and complex problem-solving. Ever since the open source DeepSeek R1 shook the industry in January with its reasoning capabilities at a much lower cost, many other Chinese models have followed suit- including Alibaba's Qwen and Moonshot AI's Kimi models. While OpenAI said at a press pre-briefing that the new open-weight models are a proactive effort to provide what users want, it is also clearly a strategic response to ramping up open source competition. Notably, OpenAI declined to benchmark its new open-weight models against Chinese open-source systems like DeepSeek or Qwen -- despite the fact that those models have recently outperformed U.S. rivals on key reasoning benchmarks. In the press briefing, the company said it confident in its benchmarks against its own models and that it would leave it to others in the AI community to test further and "make up their own minds." OpenAI's new open-weight models are built using a Mixture-of-Experts (MoE) architecture, in which the system activates only the "experts," or sub-networks, it needs for a specific input, rather than using the entire model for every query. Dylan Patel, founder of research firm SemiAnalysis, pointed out in a post on X before the release that OpenAI trained the models only using publicly known components of the architecture -- meaning the building blocks it used are already familiar to the open-source community. He emphasized that this was a deliberate choice -- that by avoiding any proprietary training techniques or architecture innovations, OpenAI could release a genuinely useful model without actually leaking any intellectual property that powers its proprietary frontier models like GPT 4o. For example, in a model card accompanying the release, OpenAI confirmed that the models use a Mixture of Experts (MoE) architecture with 12 active experts out of 64, but it does not describe the routing mechanism, which is a crucial and proprietary part of the architecture. "You want to minimize risk to your business, but you [also] want to be maximally useful to the public," Aleksa Gordic, a former Google DeepMind researcher, told Fortune, adding that companies like Meta and Mistral, which have also focused on open-weight models, also have not included proprietary information. "They minimize the IP leak and remove any risk to their core business, while at the same time sharing a useful artifact that will enable the startup ecosystem and developers," he said. "It's by definition the best they can do given those two opposing objectives."

OpenAI Drops Two Open Source AI Models That Run Locally and Match Premium Offerings - Decrypt

Performance rivals GPT-4o-mini and o3 -- beats rivals in math, code, and medical benchmarks OpenAI released two open-weight language models Tuesday that deliver performance matching its commercial offerings while running on consumer hardware -- the gpt-oss-120b needs a single 80GB GPU and the gpt-oss-20b operates on devices with just 16GB of memory. The models, available under Apache 2.0 licensing, achieve near-parity with OpenAI's o4-mini on reasoning benchmarks. The 120-billion parameter version activates only 5.1 billion parameters per token through its mixture-of-experts architecture, while the 20-billion parameter model activates 3.6 billion. Both handle context lengths up to 128,000 tokens -- the same as GPT-4o. The fact that they are released under that specific license is a pretty big deal. It means anyone can use, modify and profit from those models without restrictions. This includes anyone from you to OpenAI's competitors like Chinese startup DeepSeek. The release comes as speculation mounts about GPT-5's imminent arrival and competition intensifies in the open-source AI space. The OSS models are OpenAI's latest open-weight language models since GPT-2 in 2019. There is not really a release date for GPT-5, but Sam Altman hinted it could happen sooner than later. "We have a lot of new stuff for you over the next few days," he tweeted early today, promising "a big upgrade later this week." The open-source models that dropped today are very powerful. "These models outperform similarly sized open models on reasoning tasks, demonstrate strong tool use capabilities, and are optimized for efficient deployment on consumer hardware," OpenAI stated in its announcement. The company trained them using reinforcement learning and techniques from its o3 and other frontier systems. On Codeforces competition coding, gpt-oss-120b scored an Elo rating of 2622 with tools and 2463 without -- surpassing o4-mini's 2719 rating and approaching o3's 2706. The model hit 96.6% accuracy on AIME 2024 mathematics competitions compared to o4-mini's 87.3% and achieved 57.6% on the HealthBench evaluation, beating o3's 50.1% score. The smaller gpt-oss-20b matched or exceeded o3-mini across these benchmarks despite its size. It scored 2516 Elo on Codeforces with tools, reached 95.2% on AIME 2024, and hit 42.5% on HealthBench -- all while fitting in memory constraints that would make it viable for edge deployment. Both models support three reasoning effort levels -- low, medium, and high -- that trade latency for performance. Developers can adjust these settings with a single sentence in the system message. The models were post-trained using processes similar to o4-mini, including supervised fine-tuning and what OpenAI described as a "high-compute RL stage." But don't think just because anyone can modify those models at will, you'll have an easy time. OpenAI filtered out certain harmful data related to chemical, biological, radiological, and nuclear threats during pre-training. The post-training phase used deliberative alignment and instruction hierarchy to teach refusal of unsafe prompts and defense against prompt injections. In other words, OpenAI claims to have designed its models to make them so safe, they cannot generate harmful responses even after modifications. Eric Wallace, an OpenAI alignment expert, revealed the company conducted unprecedented safety testing before release. "We fine-tuned the models to intentionally maximize their bio and cyber capabilities," Wallace posted on X. The team curated domain-specific data for biology and trained the models in coding environments to solve capture-the-flag challenges. The adversarially fine-tuned versions underwent evaluation by three independent expert groups. "On our frontier risk evaluations, our malicious-finetuned gpt-oss underperforms OpenAI o3, a model below Preparedness High capability," Wallace stated. The testing indicated that even with robust fine-tuning using OpenAI's training stack, the models couldn't reach dangerous capability levels according to the company's Preparedness Framework. That said, the models maintain unsupervised chain-of-thought reasoning, which OpenAI said is of paramount importance for keeping a wary eye on the AI. "We did not put any direct supervision on the CoT for either gpt-oss model," the company stated. "We believe this is critical to monitor model misbehavior, deception and misuse." OpenAI hides the full chain of thought on its best models to prevent competition from replicating their results -- and to avoid another DeepSeek event, which now can happen even easier.

OpenAI releases free, downloadable models in competition catch-up

San Francisco (United States) (AFP) - OpenAI on Tuesday released two new artificial intelligence (AI) models that can be downloaded for free and altered by users, to challenge similar offerings by US and Chinese competition. The release of gpt-oss-120b and gpt-oss-20b "open-weight language models" comes as the ChatGPT-maker is under pressure to share inner workings of its software in the spirit of its origin as a nonprofit. "Going back to when we started in 2015, OpenAI's mission is to ensure AGI (Artificial General Intelligence) that benefits all of humanity," said OpenAI chief executive Sam Altman. An open-weight model, in the context of generative AI, is one in which the trained parameters are made public, enabling users to fine-tune it. Meta touts its open-source approach to AI, and Chinese AI startup DeepSeek rattled the industry with its low-cost, high-performance model boasting an open weight approach that allows users to customize the technology. "This is the first time that we're releasing an open-weight model in language in a long time, and it's really incredible," OpenAI co-founder and president Greg Brockman said during a briefing with journalists. The new, text-only models deliver strong performance at low cost, according to OpenAI, which said they are suited for AI jobs like searching the internet or executing computer code, and are designed to be easy to run on local computer systems. "We are quite hopeful that this release will enable new kinds of research and the creation of new kinds of products," Altman said. OpenAI said it is working with partners including French telecommunications giant Orange and cloud-based data platform Snowflake on real-world uses of the models. The open-weight models have been tuned to thwart being used for malicious purposes, according to OpenAI. Altman early this year said his company had been "on the wrong side of history" when it came to being open about how its technology works. He later announced that OpenAI will continue to be run as a nonprofit, abandoning a contested plan to convert into a for-profit organization. The structural issue had become a point of contention, with major investors pushing for better returns. That plan faced strong criticism from AI safety activists and co-founder Elon Musk, who sued the company he left in 2018, claiming the proposal violated its founding philosophy. In the revised plan, OpenAI's money-making arm will be open to generate profits but will remain under the nonprofit board's supervision.

For the First Time in 6 Years, OpenAI's New Models Are Free for Everyone

Developers rejoice -- OpenAI has just released two new open-weight models. Unlike most of OpenAI's models, these new products can be downloaded and used without needing to pay API fees to OpenAI. The models can even run locally on laptops and phones. To understand why this is such a big deal, it helps to understand the difference between closed models and open models. Closed models, such as OpenAI's o models or Anthropic's Claude models, can only be accessed via an API, and do not disclose information about the variables or "weights" used to determine how the AI's internal connections are wired. Open models, on the other hand, can be freely downloaded directly onto a device, and their weights are publicly available. Many frontier AI companies have shied away from releasing open models because of security concerns (they don't want to give bad actors access to a super intelligent tool), but others, such as Meta, have released several open models. In March, OpenAI CEO Sam Altman said that the company would develop an open-weight model, in a reversal from earlier statements. Altman's opinion was likely swayed by the surprise release of DeepSeek's open-weight R1 model in late 2024, which shook the AI industry and Wall Street. Now, OpenAI has released two models, and they have the memorable names of gpt-oss-120b and gpt-oss-20b. According to a blog post from OpenAI, gpt-oss-120b is roughly equivalent in intelligence to the company's o4-mini model, and can run efficiently on a single GPU with 80 gigabytes of memory. The second model, gpt-oss-20b, is significantly smaller, and can run on devices with just 16 gigabytes of memory, making it ideal for building AI capabilities into mobile devices. Both of the open models also include a feature called tool calling, which enables AI models to take actions beyond simply answering questions; they can do tasks like searching the web, executing code, and even calling upon larger cloud-based models to handle especially difficult tasks. The models also have chain-of-thought reasoning, meaning they can think through the best way to complete a task in multiple steps, a method that has been found to improve performance. Developers will even be able to adjust how much effort the models put into specific tasks. For developers, this is a big moment. Now, they can develop powerful apps without needing to pay OpenAI. People building web-based applications won't need to pay API fees to OpenAI, and will only need to pay cloud computing providers. Developers building AI features directly within hardware, such as a mobile phone or gaming device, will be able to run their features without paying anyone. One thing the models can't do? Handle multimodal tasks. They can't process or generate anything other than text, and are incapable of creating images, videos, or audio. Developers can now download the models on HuggingFace, or can access the models across a wide range of cloud computing platforms including Microsoft Azure, AWS, and LM Studio. The final deadline for the 2025 Inc. Power Partner Awards is this Friday, August 8, at 11:59 p.m. PT. Apply now.

OpenAI Finally Releases Its First Open-Source AI Models Since 2019

OpenAI says both models are built on a mixture-of-experts architecture OpenAI released two open-source artificial intelligence (AI) models on Tuesday. This marks the San Francisco-based AI firm's first contribution to the open community since 2019, when GPT-2 was open sourced. The two new models, dubbed gpt-oss-120b and gpt-oss-20b, are said to offer comparable performance to the o3 and o3-mini models. Built on the mixture-of-experts (MoE) architecture, the company says these AI models have undergone rigorous safety training and evaluation. The open weights of these models are available to download via Hugging Face. In a post on X (formerly Twitter), OpenAI CEO Sam Altman announced the release of these models, highlighting that "gpt-oss-120b performs about as well as o3 on challenging health issues." Notably, both the models are currently being hosted on OpenAI's Hugging Face listing, and interested individuals can download and locally run the available open weights. On its website, OpenAI explains that these models are compatible with the company's Responses application programming interface (API), and can work with agentic workflows. These models also support tool use such as web search or Python code execution. With native reasoning, the models also display transparent chain-of-thought (CoT), which can be adjusted to either focus on high-quality responses or low latency outputs. Coming to the architecture, these models are built on MoE architecture to reduce the number of active parameters for processing efficiency. The gpt-oss-120b activates 5.1 billion parameters per token, while gpt-oss-20b activates 3.6b parameters per token. The former has a total of 117 billion parameters and the latter has 21 billion parameters. Both models support a content length of 1,28,000 tokens. These open-source AI models were trained on mostly English language text database. The company focused on Science, Technology, Engineering, and Mathematics (STEM) fields, coding, and general knowledge. In the post-training stage, OpenAI used reinforcement learning (RL)-based fine-tuning. Benchmark performance of the open-source OpenAI models Photo Credit: OpenAI Based on the company's internal testing, gpt-oss-120b outperforms o3-mini on competition coding (Codeforces), general problem solving (MMLU and Humanity's Last Exam), and tool calling (TauBench). But in general, these models marginally fall short of o3 and o3-mini on other benchmarks such as GPQA Diamond. OpenAI highlights that these models have undergone intensive safety training. In the pre-training stage, the company filtered out harmful data relating chemical, biological, radiological, and nuclear (CBRN) threats. The AI firm also said that it used specific techniques to ensure the model refuses unsafe prompts and is protected from prompt injections. Despite being open-source, OpenAI claims that the models have been trained in a way that they cannot be fine-tuned by a bad actor to provide harmful outputs.

OpenAI responds to DeepSeek with two 'open' models

OpenAI is launching GPT-oss-120b and GPT-oss-20b, two open-weight AI models capable of reasoning and complex tasks like coding and online information retrieval. Available on Hugging Face, these models offer text generation capabilities but lack image or video support. While OpenAI discloses the models' numerical parameters, the training data remains proprietary, differing from a fully open-source approach. OpenAI is releasing a pair of open and freely available artificial intelligence models that can mimic the human process of reasoning, months after China's DeepSeek gained global attention with its own open AI software. The two models, called GPT-oss-120b and GPT-oss-20b, will be available on AI software hosting platform Hugging Face and can produce text - but not images or videos-in response to user prompts, OpenAI said on Tuesday. These models can also carry out complex tasks like writing code and looking up information online on a user's behalf, the company said. Crucially, the models are both open-weight systems, similar to Meta Platforms Inc.'s Llama. The term "weight" refers to the parameters in an AI model. OpenAI is disclosing the many numerical values the models picked up and were tweaked with during the training process, allowing developers to better customise them. However, OpenAI is not revealing the data used to train them, falling short of the definition for a truly open source AI model. Despite its name, most of OpenAI's models are closed systems -the kind of software that's controlled by the developer, can't be modified by users, and includes less transparency about its technical underpinnings. Like many of its US rivals, OpenAI has guarded its training data and focused on charging more for its most powerful models to offset the immense cost of development.

OpenAI launches its first open-weight models since GPT-2 - The Economic Times

OpenAI has released two new open-weight AI reasoning models, its first in over five years. The larger model, gpt-oss-120b, can run on a single Nvidia GPU, while the smaller, gpt-oss-20b, can run on a laptop. Both are available to download from Hugging Face and are designed for accessibility.OpenAI has launched two new open-weight AI reasoning models, marking its first release of this kind since GPT-2 over five years ago. Announced on Tuesday, the models are now available for download on the developer platform Hugging Face. The new models, gpt-oss-120b and gpt-oss-20b, are designed with accessibility in mind. The larger model, gpt-oss-120b, can run on a single Nvidia GPU, while the smaller gpt-oss-20b can operate on a consumer laptop with just 16GB of memory, OpenAI said. Unlike open-source models, which include access to the full training data, source code, and development methods, open-weight models make only the trained parameters or weights publicly available. This allows developers to analyse and fine-tune the models for specific tasks without needing the original training data. "One of the things that is unique about open models is that people can run them locally. People can run them behind their own firewall, on their own infrastructure," said OpenAI cofounder Greg Brockman during a press briefing, according to Reuters The two models are particularly strong in areas such as coding, advanced mathematics, and health-related queries. However, they are text-only, meaning they cannot handle images or audio like OpenAI's other models. These open models also tend to hallucinate (generate incorrect or misleading content) more often than OpenAI's latest reasoning models, o3 and o4-mini. OpenAI's earlier years saw it lean towards open-sourcing its work, but the company has since largely adopted a closed-source approach. This strategy has supported the growth of its commercial offerings, including paid API access for businesses and developers. That said, CEO Sam Altman admitted earlier this year that OpenAI may have taken the wrong stance. He said the company has been "on the wrong side of history" when it comes to open-sourcing its technology. In a letter responding to former US President Donald Trump's AI Action Plan, OpenAI wrote: "We believe the question of whether AI should be open or closed source is a false choice -- we need both, and they can work in a complementary way that encourages the building of AI on American rails."

OpenAI launches open-weight models GPT OSS 120b & GOT OSS 20b: Check how to get new models on your computer

OpenAI has released gpt-oss-120b and gpt-oss-20b, its first open-weight AI reasoning models since 2019, amid rising competition. These models, available on Hugging Face and Azure AI Foundry, allow developers to run and adapt them locally or via the cloud. This shift provides greater transparency and customization, enabling businesses to deploy AI applications with enhanced security and control. OpenAI has launched two open-weight AI reasoning models, gpt-oss-120b and gpt-oss-20b, for public use, marking its first open release since GPT-2 in 2019. The move comes amid growing pressure from global developers and rising competition from China's DeepSeek and Meta's Llama, which have embraced open-source approaches. The released gpt-oss models are classified as 'open-weight' -- not fully open-source. While open-source models provide full access to source code, architecture, training data, and allow modifications, open-weight models only provide the trained weights. This means users can run and adapt the model, but cannot see or change its training details or code. This distinction affects how much transparency and customisation developers can expect. OpenAI's decision to release open-weight, instead of open-source, reflects a compromise between openness and maintaining control over the core technology. Developers can access OpenAI's gpt-oss-120b and gpt-oss-20b models by downloading them from Hugging Face. The gpt-oss models can be run on personal laptops or single Nvidia GPUs, offering developers the ability to deploy them locally or through Microsoft's Azure cloud platform. This is a major shift for OpenAI, which has primarily focused on proprietary AI models since the release of GPT-3. These models are also integrated into Microsoft's Azure AI Foundry platform, where users can deploy them with a few command-line instructions. For those preferring offline or local deployment, the gpt-oss-20b model is available via Foundry Local on Windows devices and will soon be supported on MacOS. Developers can follow the QuickStart guides provided by Microsoft to run the models, fine-tune them using their own data, or deploy them across cloud and edge environments depending on their performance and privacy needs. The shift in OpenAI's approach comes after criticism and increased competition in the AI space. China's DeepSeek, an open-source language model, gained global attention for offering similar capabilities at significantly lower development costs. Meta's Llama models also followed the open-weight route and have crossed over a billion downloads, although some developers have raised concerns over their licensing terms. Earlier this year, OpenAI CEO Sam Altman acknowledged the need for a new approach. In a Reddit Q&A, Altman said, "[I personally think we need to] figure out a different open source strategy. Not everyone at OpenAI shares this view, and it's also not our current highest priority... We will produce better models, but we will maintain less of a lead than we did in previous years." The gpt-oss-120b model is designed for complex reasoning tasks such as code generation, math problems, and domain-specific queries. It contains 120 billion parameters and can run on a single enterprise-level GPU. The gpt-oss-20b model is lightweight and can run on consumer laptops with 16GB of memory. It is suited for local inference tasks like code execution and autonomous workflows. Both models are supported by Microsoft's Azure AI Foundry platform, which allows developers to fine-tune, adapt, and deploy them on both cloud and local systems. The gpt-oss-20b model will also be supported on Windows and soon on MacOS. Microsoft, OpenAI's key partner, announced the integration of the gpt-oss models into its Azure AI Foundry and Windows AI Foundry platforms. According to Microsoft, this enables developers to run these models securely and efficiently across cloud, edge, and offline environments. "With the launch of OpenAI's gpt-oss models -- its first open-weight release since GPT-2 -- we're giving developers and enterprises unprecedented ability to run, adapt, and deploy OpenAI models entirely on their own terms," Microsoft said. For developers, having access to open weights means greater transparency and the ability to customise models for specific use cases. They can fine-tune models using techniques like LoRA and PEFT, integrate their own data, and deploy across different environments. For businesses, this change provides flexibility and control. Without relying solely on cloud-based tools or black-box APIs, they can now host and deploy AI applications with greater security, data sovereignty, and cost control. The gpt-oss models are currently available via Azure AI Foundry. Developers can deploy them using command-line tools, fine-tune them with proprietary data, or integrate them into hybrid cloud and edge environments. Foundry Local supports gpt-oss-20b for on-device inference across CPUs, GPUs, and NPUs. OpenAI's new direction is part of a broader trend in the AI industry towards balancing openness with responsible deployment, while supporting innovation across both proprietary and open ecosystems. (You can now subscribe to our Economic Times WhatsApp channel)

OpenAI releases two 'open' AI models after DeepSeek's success

OpenAI is releasing a pair of open and freely available artificial intelligence models that can mimic the human process of reasoning, months after China's DeepSeek gained global attention with its own open AI software. The two models, called GPT-oss-120b and GPT-oss-20b, will be available on AI software hosting platform Hugging Face and can produce text -- but not images or videos -- in response to user prompts, OpenAI said on Tuesday. These models can also carry out complex tasks like writing code and looking up information online on a user's behalf, the company said. Crucially, the models are both open-weight systems, similar to Meta Platforms' Llama. The term "weight" refers to the parameters in an AI model. OpenAI is disclosing the many numerical values the models picked up and were tweaked with during the training process, allowing developers to better customize them. However, OpenAI is not revealing the data used to train them, falling short of the definition for a truly open-source AI model.

OpenAI Unleashes First Open-Weight Models Since GPT-2, Fully Free Under Apache 2.0, With Ability to Run Locally, 128K Context, And Unmatched Customization

In a major leap for the AI industry, OpenAI has officially launched its first set of open-weight models, which marks a pivotal step in introducing transparency and bringing more developer freedom. The two new models, gpt-oss-20b and gpt-oss-120b, are the company's first proper open-weight release after GPT-2 in 2019, and we have been seeing years of closed systems up until now. These two tools are available for free download and can run directly on any hardware with sufficient memory, including Macs with Apple Silicon, denoting a shift in the company's ongoing approach as developers can run AI models locally without the need for servers or APIs. The open-weight language models can be downloaded for free and just went live on GitHub, so developers can immediately gain access to the model weights and inference code under the name GPT-OSS. The models are, however, accompanied by a Complimentary Use Policy so that while the company is focusing on moving away from AI tools being locked behind proprietary walls and giving tech enthusiasts the freedom to run the systems locally without the need for an internet connection or even a server-based API access, it is still maintaining a measured approach. OpenAI states: We aim for our tools to be used safely, responsibly, and democratically, while maximizing your control over how you use them. By using OpenAI gpt-oss-120b and gpt-oss-20b, you agree to comply with all applicable law. So, the legal language the AI company carries is clear: although OpenAI is opening its gates, it is still determined to comply with the laws. But beyond this fine print, this step is monumental because, unlike API-dependent models such as ChatGPT, the new system can run locally on machines with sufficient resources. Developers can build applications without experiencing any latency or dealing with any surveillance issues since it is raw, foundational AI that you have complete control over. If we are to see the technical aspects of these models at a glance, they are rather impressive. The gpt-oss-20b is a 20-billion parameter dense model, while the gpt-oss-120b is a 120-billion parameter MoE model that allows for more computational efficiency and lower inference. The models were additionally trained on a 1.8 trillion-token dataset that includes licensed resources and publicly available data. With a 128K token context window, both models are able to handle complex reasoning and agentic capabilities. The community's reactions to this huge step by OpenAI are largely positive, and many AI researchers are suggesting that OpenAI is focusing on making openness the norm. Noam Brown, a prominent researcher, called this move a step into a multipolar AI ecosystem. Some critics were quick to point out how OpenAI has been late to the open-weight game, with other tech giants such as Meta and Mistral establishing a strong position when it comes to open access. OpenAI's decision nonetheless sends a strong message: it is listening to the developer community and focusing more on transparency and access. One of the most impactful aspects of the release is the Apache 2.0 license under which both models are being offered, which gives developers and other organizations a permissive license to use the models for both commercial and research purposes without any legal hurdles looming. OpenAI taking this leap is vital as it signals a reimagined AI ecosystem where users can customize and deploy local systems without being tied to prohibitive commercial licenses. OpenAI, by moving ahead in this domain, is putting foundational tools in the hands of the public and not behind a paywall, marking a huge shift in the philosophical and strategic direction. This is a win in terms of empowering researchers and developers in the AI space. While it does not turn the company into an open-source company overnight, it is indeed a welcome move, giving the community hope for the beginning of the company's evolving landscape.

OpenAI Returns to Open Source Roots, Releases 120B and 20B AI Models

Both AI models are released under the Apache 2.0 license and can be downloaded from Hugging Face, Ollama, LM Studio, and more. OpenAI has finally released two open-weight AI models called gpt-oss-120b and gpt-oss-20b since the release of GPT-2 in 2019, in a major shift. OpenAI claims that its new open-weight AI models deliver state-of-the-art performance in the open-source arena, outperforming similarly sized models. Both are MoE (Mixture of Experts) reasoning models with 5.1B and 3.6B active parameters. The gpt-oss-120b AI model achieves performance along the lines of o4-mini, and the smaller gpt-oss-20b model is comparable to o3-mini. In its blog post, OpenAI writes, "They were trained using a mix of reinforcement learning and techniques informed by OpenAI's most advanced internal models, including o3 and other frontier systems." Users and developers can run the gpt-oss-120b AI model locally on a single GPU with 80GB VRAM. And the smaller gpt-oss-20b model can run on laptops and smartphones with just 16GB of memory. In addition, these new OpenAI models have a context length of 128k tokens. OpenAI says these models were trained largely on the English dataset, and excel at STEM, coding, and general knowledge. What is interesting is that both open-weight models support agentic workflows, including tool use like web search and Python code execution. It means that you can use these models locally to complete tasks on your computer without requiring an internet connection. Talking about benchmarks, the larger gpt-oss-120b model nearly matches OpenAI's flagship o3 model on Codeforces. In the challenging Humanity's Last Exam benchmark, gpt-oss-120b scores 19% and o3 achieves 24.9% with tools access. Next, in GPQA Diamond, gpt-oss-120b got 80.1% while o3 scored 83.3%. Looking at the benchmarks, OpenAI's open-weight models do look powerful. It would be interesting to see how well gpt-oss-120b and gpt-oss-20b perform against Chinese open-weight AI models such as Qwen and DeepSeek. Both models are released under the Apache 2.0 license and can be downloaded from Hugging Face (120b | 20b).

OpenAI GPT-OSS Open-Source AI : Benefits, Risks and Industry Impact

What if the most advanced AI tools were no longer locked behind corporate walls, but instead placed directly in the hands of developers, researchers, and innovators around the world? That's exactly what OpenAI has done by releasing its new 120B and 20B parameter models as open-weight systems under the Apache 2.0 license. This bold move challenges the status quo of proprietary AI, offering unprecedented access to models capable of tackling complex reasoning, coding, and STEM tasks. With this release, OpenAI is not just sharing technology -- it's reshaping the AI landscape by prioritizing transparency, collaboration, and accessibility over exclusivity. Prompt Engineering explores how these open-weight models are more than just technical achievements -- they represent a shift toward a more inclusive and innovative AI ecosystem. From their sparse architecture that optimizes efficiency to their extended context length of 128,000 tokens, these models are designed to meet the needs of both innovative research and practical applications. Whether you're curious about how the 20B model provide widespread access tos AI for consumer-grade hardware or intrigued by the ethical safeguards OpenAI has implemented, this release raises critical questions about the future of AI. Could this be the beginning of a new era where the power of AI is shared, not siloed? The two models are designed to cater to a broad spectrum of hardware capabilities, making sure accessibility for users with varying computational resources. Both models employ a sparse architecture, activating only a fraction of their parameters during operation -- approximately 5 billion for the 120B model and 3.6 billion for the 20B model. This design enhances computational efficiency while maintaining high performance. A standout feature is the extended context length of up to 128,000 tokens, enabled by rotary positional embeddings. This capability is particularly beneficial for tasks such as long-form reasoning, analyzing extensive documents, or handling complex coding projects. Additionally, the models are primarily trained on English text, with a strong emphasis on reasoning, coding, and mathematical problem-solving, making them versatile tools for developers and researchers. The open-weight models deliver performance on par with proprietary systems like GPT-4 mini, particularly in areas such as reasoning, tool usage, and problem-solving. One of their most practical features is the ability to adjust reasoning effort levels -- low, medium, or high -- based on computational needs. This flexibility allows you to optimize performance for specific tasks while managing resource constraints effectively. Another notable feature is the integration of chain-of-thought reasoning. This functionality enhances the models' problem-solving capabilities by breaking down complex tasks into manageable steps. It also provides transparency, allowing you to trace and debug the reasoning process. This level of insight is invaluable for applications requiring precision, reliability, and accountability. Find more information on open-weight AI models by browsing our extensive range of articles, guides and tutorials. Efficiency is a core principle of these models. Both use 4-bit floating-point precision, significantly reducing memory requirements without compromising computational accuracy. This innovation ensures that the models can operate efficiently across a variety of platforms and environments. The models are compatible with a wide range of platforms, including: Support for PyTorch and Apple Metal further broadens their applicability, allowing seamless integration into existing workflows. This compatibility ensures that you can deploy the models across diverse environments with minimal technical barriers, making them highly adaptable for various use cases. OpenAI has prioritized safety and transparency in this release. To address potential misuse, the organization has launched a red-teaming challenge with a $500,000 prize fund. This initiative invites experts to identify vulnerabilities and propose solutions, fostering a collaborative approach to safety and ethical AI development. The models' chain-of-thought reasoning operates without direct supervision, allowing you to independently verify and understand their decision-making processes. This transparency is essential for building trust and making sure the responsible use of AI technologies. By providing tools to trace and debug reasoning, OpenAI enables users to maintain accountability and reliability in their applications. The open-weight models are readily available on platforms like Hugging Face, accompanied by comprehensive APIs and integration tools. This accessibility lowers barriers to adoption, particularly for resource-constrained sectors and emerging markets. Partnerships with industry leaders such as Nvidia, AMD, and AWS further enhance the models' utility, providing robust support for deployment and scaling. To assist adoption, OpenAI has released detailed documentation, cookbooks, and training guides. These resources are designed to help you quickly understand and implement the models, regardless of your technical expertise. This collaborative approach reflects a broader trend in the AI community, where open development is increasingly viewed as a pathway to innovation and safety. The release of these open-weight models marks a pivotal moment in AI development. By lowering the barriers to entry, OpenAI enables a wider range of organizations and individuals to use advanced AI technologies. This widespread access of access is particularly impactful for emerging markets and sectors with limited resources, allowing them to benefit from innovative AI capabilities. The open source nature of these models also fosters innovation and safer AI development. By encouraging collaboration and transparency, OpenAI sets a precedent for responsible AI practices. This initiative aligns with global efforts to ensure ethical development and deployment of AI technologies, highlighting the importance of shared responsibility in shaping the future of artificial intelligence.

The US Makes Its First Modern Foray Into Open-Source Models With GPT-OSS, But How Does It Stack Up Against Chinese Counterparts? Hint: Pretty Close

OpenAI finally released open-weight models a few days ago, marking its entry into a segment currently heavily dominated by Chinese AI models. Well, it seems like American AI companies have now started to follow what their Chinese counterparts have been doing for several years, which is integrating an open-source ecosystem with LLMs. Interestingly, in President Trump's 'AI action' plan, a priority has been given to open-source AI models, and OpenAI has actually followed this by releasing the gpt-oss models. For those unaware, they are its first set of open-weight models since GPT-2, and they mainly come in two different configurations, the gpt-oss-20b and gpt-oss-120b. Diving into the specifics of OpenAI's latest open-weight models, the gpt-oss-20b features a 21 billion parameter count, with an MoE transformer. More importantly, it offers a context window of up to 131,072 tokens and can run effectively on 16GB VRAM platforms, so most modern-day consumer GPUs will easily run it locally. On the other hand, the gpt-oss-120b is a larger open-weight model, with a 117 billion parameter count, and features strong reasoning performance, which is why to run it, you need at least a single NVIDIA H100 platform. More importantly, these models are released under the Apache 2.0 license, a permissive license allowing commercial use, modification, and redistribution. This gives them a fully open-source nature, similar to Chinese counterparts. For OpenAI, this release is one-of-a-kind and probably targeted towards Chinese developments. When you look at it, Chinese AI firms like DeepSeek, Alibaba and many others have an open-source environment running for several years now, while in the US, apart from Meta's LLaMA, little mainstream models have made their way into such an ecosystem. So, now that OpenAI has finally decided to include open-weight models, we could expect new releases from them as well, but for now, let's compare the gpt-oss with Chinese alternatives. When you take the parameter count as the metric, Chinese alternatives beat OpenAI's options by a huge margin, with models like the DeepSeek V2, Qwen 3, and many others having higher figures, evenwith active parameters as well. Considering China's top AI models from DeepSeek and Alibaba, here's how things pan out: The total/active parameter count isn't the only deciding factor in determining whether a model is superior, but just for PR purposes, Chinese models do have a considerable edge over OpenAI right now, mainly because they have been in the game for several years now. Now, let's factor in the real-time performance of these AI models across several well-known workloads such as MMLU (Massive Multitask Language Understanding), AIME Math (American Invitational Mathematics Exam) and many others, which we have taken from testing by Clarifai. This shows that gpt-oss beats the competition in reasoning workloads by a huge margin, and the same is true for mathematical operations. Moreover, it has a smaller active parameter footprint than many dense models, allowing for more cost-effective options for those who want to use the AI model locally. However, the benchmarks do indicate that for agentic workloads and multilingual capability, gpt-oss-120b does lag behind Chinese alternatives, but it is still a top-tier choice for this ecosystem. Open-weight models are the way to go in the AI industry since they bring several benefits to the general ecosystem. OpenAI's efforts will definitely strengthen the position of the US in this segment, which previously had been dominated by Chinese AI companies. Sam Altman and his team would definitely be happy with the results.

OpenAI's GPT-OSS : Semi Open Source Models for Local AI Applications

What happens when a tech giant decides to rewrite the rules of the game? OpenAI's recent release of GPT-OSS, a pair of powerful open weight semi source language models, has sent shockwaves through the artificial intelligence industry. By making these models freely available under the permissive Apache 2.0 license, OpenAI has not only challenged the dominance of proprietary systems but also ignited a fierce debate about the future of AI. On one hand, this bold move promises to provide widespread access to access to innovative technology, empowering developers and researchers worldwide. On the other, it raises pressing concerns about safety and misuse, as the open-weight distribution of these models makes them impossible to retract. In a field where control and exclusivity have long been the norm, OpenAI's decision is nothing short of innovative -- and deeply polarizing. Wes Roth explores how GPT-OSS is poised to reshape the AI landscape, from lowering barriers for resource-constrained innovators to sparking new waves of collaboration and commercial opportunity. You'll discover the technical breakthroughs that make these models both powerful and practical, as well as the risks and responsibilities that come with open source AI. Whether you're a developer eager to experiment with innovative tools or a skeptic questioning the ethical implications, this release demands attention. As we unpack the broader implications of OpenAI's gamble, one question looms large: can the benefits of open source AI outweigh the risks, or has OpenAI opened a Pandora's box? The GPT-OSS models stand out for their ability to deliver high performance across a range of tasks, making them competitive with leading proprietary systems locally on your home computer or business network. Both models exhibit strengths in areas such as tool use, chain-of-thought reasoning, and instruction-following. These capabilities make them versatile tools for applications ranging from academic research to practical problem-solving in real-world scenarios. The development of GPT-OSS models incorporates advanced training methodologies and optimization techniques, making sure both power and practicality. These innovations make GPT-OSS models not only powerful but also practical for deployment in environments with limited computational resources, broadening their potential applications. By releasing GPT-OSS under the Apache 2.0 license, OpenAI has opened the door to widespread commercial use, modification, and local deployment. This decision carries significant implications for the AI industry and beyond: This widespread access of AI tools has the potential to reshape industries, accelerate technological progress, and empower a broader range of users to use AI for innovation. While the open source release of GPT-OSS offers numerous benefits, it also introduces significant risks. The open-weight distribution of these models means they cannot be recalled, raising concerns about potential misuse. OpenAI has acknowledged these risks and advocates for the development of monitoring systems to track and mitigate harmful behavior. However, the responsibility for making sure safe usage largely falls on the broader research community, developers, and individual users. OpenAI's decision to release GPT-OSS aligns with broader efforts to maintain leadership in open source AI development. This move contrasts with recent trends of companies retreating from open source commitments, signaling a return to OpenAI's foundational mission of providing widespread access to AI. By making these models freely available, OpenAI is fostering a competitive shift in the AI industry. This decision challenges proprietary systems, promotes accessibility, and encourages innovation, potentially reshaping the competitive dynamics of AI development. It also reinforces the importance of collaboration and openness in driving technological progress. As the AI ecosystem continues to evolve, OpenAI's release of GPT-OSS models represents a pivotal moment. While the industry anticipates the arrival of GPT-5, which is expected to surpass GPT-OSS in capabilities, the release of these open source models has already redefined the competitive landscape. OpenAI's focus on accessibility and decentralization is advancing technological innovation while promoting collaboration. However, the ongoing challenge will be to balance the benefits of open source AI with the need for safety and ethical considerations in its application.

OpenAI's New Open Models Overview : GPT-OSS 120B and 20B

What if the power of innovative AI wasn't locked behind proprietary walls but placed directly in the hands of developers, researchers, and innovators? OpenAI's latest release, GPT-OSS 120B and 20B, represents a bold step toward this vision. With their open-weight design and licensing under Apache 2.0, these models aim to bridge the gap between exclusivity and accessibility, offering developers the freedom to customize and deploy advanced AI systems without sacrificing performance. Whether you're running enterprise-grade cloud applications or experimenting on local hardware, these models promise to redefine what's possible in AI-driven development. Sam Witteveen explains the unique capabilities and trade-offs of the GPT-OSS models, from their scalable architecture to their new integration features. You'll discover how these tools empower developers to balance computational efficiency with task complexity, and why their open-weight framework could signal a paradigm shift in the AI landscape. But are they truly the providing widespread access to force they claim to be, or do their limitations -- like restricted multilingual support and slower high-reasoning performance -- temper their promise? Let's unpack the potential and challenges of these fantastic models, and what they mean for the future of AI innovation. The GPT-OSS models are available in two configurations, each tailored to meet specific deployment needs: Both models use advanced training techniques, including reinforcement learning, supervised learning, and instruction tuning. These methods enhance their ability to perform complex reasoning and execute tasks effectively. Additionally, the models offer adjustable reasoning levels -- low, medium, and high -- allowing you to balance computational latency with task performance. For example, high reasoning levels improve accuracy in complex tasks but may result in slower response times, making them ideal for precision-critical applications. The GPT-OSS models are released under the Apache 2.0 license, granting you broad rights to use, modify, and redistribute them. However, while the models are labeled as "open-weight," they are not fully open source. OpenAI has not provided access to the training code or datasets, which limits the ability to reproduce the models independently. This approach reflects OpenAI's effort to enhance accessibility while safeguarding proprietary research and intellectual property. For developers, this licensing model offers significant flexibility. You can integrate the models into your projects, customize them to suit specific requirements, and even redistribute modified versions, all while adhering to the terms of the Apache 2.0 license. The GPT-OSS models are designed to support a wide range of advanced functionalities, making them versatile tools for developers. Key features include: With a context length of up to 128,000 tokens, the models are particularly effective in tasks requiring extensive input processing. This includes document summarization, multi-turn conversations, and complex data analysis. Their architecture incorporates rotary positional embeddings and a mixture-of-experts framework, enhancing their reasoning and generalization capabilities. However, their current support is limited to English, which may restrict their use in multilingual contexts. Benchmark testing reveals that the GPT-OSS models perform competitively in reasoning and function-calling tasks. While they may not fully match the performance of proprietary OpenAI models in every area, they demonstrate strong capabilities in handling complex reasoning challenges. This makes them particularly valuable for applications in research, education, and enterprise solutions. However, there are trade-offs to consider. Higher reasoning levels improve accuracy but can lead to increased response times, which may not be ideal for real-time applications. For time-sensitive tasks, lower reasoning levels may offer a better balance between speed and performance. Understanding these trade-offs is essential for optimizing the models' use in your specific applications. The GPT-OSS models are designed to accommodate diverse deployment scenarios, offering flexibility for developers with varying needs: Both models integrate seamlessly with OpenAI's Harmony SDK and OpenRouter API, simplifying the process of incorporating them into existing systems. This ease of integration allows you to focus on building innovative applications without being bogged down by complex deployment challenges. Despite their strengths, the GPT-OSS models have several limitations that you should be aware of: These limitations underscore the importance of carefully evaluating your specific use case to determine whether the GPT-OSS models align with your requirements. By understanding their capabilities and constraints, you can make informed decisions about how to best use these tools in your projects. The release of GPT-OSS 120B and 20B marks a significant milestone in OpenAI's efforts to balance proprietary advancements with open contributions. By making these models accessible under an open-weight framework, OpenAI fosters innovation and competition within the AI community. For developers like you, this represents an opportunity to use innovative AI technologies while retaining control over deployment and customization. As other organizations consider adopting similar approaches, the release of these models could signal a broader shift toward more accessible AI development. Whether you are building applications for research, business, or personal use, the GPT-OSS models provide a powerful foundation to explore new possibilities in artificial intelligence.

OpenAI Takes on Rivals Meta, DeepSeek With 2 New Models | PYMNTS.com

By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. OpenAI's new gpt-oss models come in two sizes: 120 billion and 20 billion parameters, or the statistical relationships learned by a model during training. Generally speaking, the higher the parameter count, the more capable the model. "We believe this is the best and most usable open model in the world," OpenAI CEO Sam Altman said in a post on X. The last time OpenAI released an open model was in 2019, with GPT-2. But GPT-2 was fully open -- making it truly open-source -- albeit done in stages over months. OpenAI didn't say whether gpt-oss will eventually be open-source as well. Gpt-oss is a text-only, open-weights model, meaning the user can use and fine-tune the model but not know how it was trained or what data it was trained on. Without knowing the data that went into the model, companies do not get full transparency, which could add risks to companies in financial services, healthcare and other highly regulated industries. For example, a healthcare company might want to fully vet a model's training process before deploying it on personal patient data. "If only open weights are available, developers ... lack the ability to meaningfully evaluate biases, limitations, and societal impacts," according to the Prompt Engineering and AI Institute. However, OpenAI is granting access under the Apache 2.0 license, which gives the user "perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright" to produce and distribute it. "These open models also lower barriers for emerging markets, resource-constrained sectors, and smaller organizations that may lack the budget or flexibility to adopt proprietary models," according to OpenAI. Major contenders for dominance in the open-source arena are Meta, with its Llama flagship of models but with usage and redistribution restrictions; France's Mistral AI; and Chinese providers such as DeepSeek and Alibaba for most of its Qwen models. Altman called gpt-oss "a big deal," with "strong real-world performance" comparable to o4-mini. (The o, or omni, series comprises OpenAI's reasoning models -- a new family of models after its GPT series.) For example, in the MMLU benchmark that tests how well LLMs perform a broad range of academic and professional tasks, gpt-oss is not far behind OpenAI o3 and o4-mini in performance. OpenAI said the gpt-oss 120-billion parameter model can run on the user's own computer and the smaller model can be run on a smartphone. Typically, AI models especially larger ones, run in the cloud. While releasing an open model means bad actors can use it for evil, Altman said the company thinks "far more good than bad will come from it." This is a departure from 2019, when OpenAI released GPT-2 in stages, fearful that it would used for ill. The rest of OpenAI's models are closed and proprietary. Its rivals have released varying degrees of open models: Google has open-weight, not open-source, models like Gemma. Anthropic doesn't have an open model. Microsoft open-sourced its Phi models. Amazon's models are proprietary. OpenAI Raises New Funding to Hit $300 Billion Valuation

OpenAI launches gpt-oss-120b and gpt-oss-20b open-weight AI models

OpenAI has introduced two open-weight models, gpt-oss-120b and gpt-oss-20b, under the Apache 2.0 license. These models deliver advanced reasoning capabilities, efficient deployment options, and support a broad range of use cases, including on-device inference. OpenAI aims to broaden access to powerful AI systems by releasing models that developers can run, inspect, and customize. These models provide alternatives to proprietary systems, enabling safer deployment, research, and innovation, especially in sectors and regions with limited resources. They also support local inference, global access, and transparent AI development. Both models use Transformer architecture enhanced with Mixture-of-Experts (MoE). They support context lengths up to 128k tokens using Rotary Positional Embeddings (RoPE), alongside grouped multi-query attention and locally banded sparse attention for efficient processing. The gpt-oss-120b matches OpenAI's o4-mini and outperforms o3-mini on many reasoning benchmarks, while the smaller gpt-oss-20b meets or exceeds o3-mini in various tests. The models underwent supervised fine-tuning followed by high-compute reinforcement learning (RL) stages. They support: OpenAI applied several safety measures: Note: Chain-of-thought outputs are not supervised and may contain hallucinations or harmful content. Developers should avoid exposing CoTs directly to end users. To boost safety studies, OpenAI launched a $500K Red Teaming Challenge, releasing findings and evaluation data openly. Deployment support includes: GPU-optimized versions are also available for Windows devices via VS Code and Foundry Local using ONNX Runtime. Qualcomm Technologies is supporting OpenAI's GPT-OSS models on Snapdragon platforms, with deployment expected to begin in 2025. The gpt-oss-20b model runs entirely on Snapdragon-powered devices, enabling local AI inference without relying on cloud services. Early integration testing with Qualcomm's AI Engine and AI Stack has demonstrated the model's capability to perform complex reasoning fully on-device. Developers will access the model through platforms like Hugging Face and Ollama, which integrates a lightweight open-source LLM servicing framework tailored for Snapdragon devices. Key highlights: Qualcomm expects ongoing improvements in mobile memory and software efficiency to drive growth in on-device AI, enabling more private, low-latency, and personalized AI experiences.

New OpenAI Free GPT-OSS Models Outperform DeepSeek's R1

OpenAI has launched two new open-weight language models, gpt-oss-120b and gpt-oss-20b, in a shift that marks its most significant open release since GPT-2 in 2019. Both the AI models outperform DeepSeek's R1 model and are freely available for download on Hugging Face under the permissive Apache 2.0 license. A single Nvidia 80GB GPU can run the larger gpt-oss-120b, while consumer devices with 16GB of memory can run the lighter gpt-oss-20b. The move comes at a time of increasing pressure on U.S. AI firms to open source more technology, both to compete with Chinese labs and to align with democratic values. The Trump administration recently called for more open AI models to encourage global adoption based on American principles. This release marks a notable departure from OpenAI's closed-model strategy in recent years, which helped it build a profitable business offering proprietary APIs. CEO Sam Altman acknowledged this change in direction earlier this year, saying: "I personally think we have been on the wrong side of history here and need to figure out a different open-source strategy." In a statement shared with TechCrunch, Altman added, "We are excited for the world to be building on an open AI stack created in the United States, based on democratic values, available for free to all and for wide benefit." OpenAI says the new AI models excel in reasoning, structured output, and tool use. They were trained using similar methods as their proprietary O-series models, including reinforcement learning and Mixture of Experts (MoE) architecture, which activates only a fraction of parameters per token. On benchmarks, gpt-oss-120b scored 2622 on Codeforces with tools, outperforming open competitors like DeepSeek's R1 but falling short of OpenAI's own o4-mini. On Humanity's Last Exam (HLE), gpt-oss-120b and gpt-oss-20b score 19% and 17.3%, respectively, surpassing major open-weight models but underperforming proprietary ones. However, hallucination remains a concern. OpenAI found that gpt-oss-120b and gpt-oss-20b hallucinated answers in 49% and 53% of questions, respectively, on its PersonQA benchmark, significantly higher than o4-mini's 36%. "This is expected, as smaller models have less world knowledge than larger frontier models and tend to hallucinate more," the company explained. Both models are text-only and cannot generate images or audio. While they can initiate tool use, such as web search or Python execution, they cannot process multimedia input. OpenAI has not released the training data and defends this decision amid ongoing legal scrutiny over data sourcing. This approach contrasts with fully open-source models from labs like AI2, which publish both weights and training datasets. OpenAI says it conducted adversarial testing to examine whether the AI models could be fine-tuned for harmful use, including cyberattacks or the development of biological agents. The company concluded that while there may be marginal increases in biological capabilities, the AI models do not reach the internal danger threshold, even after extensive fine-tuning. The release comes as U.S. AI firms face rising competition from Chinese AI labs like DeepSeek, Moonshot AI, and Qwen, which have made rapid progress with their open-weight models. OpenAI's gpt-oss series may now be the most capable openly available reasoning model, but expectations are high for upcoming releases from DeepSeek (R2) and Meta's Superintelligence Lab. To encourage broader safety testing, OpenAI has also launched a Red Teaming Challenge with a $500,000 prize pool, inviting researchers to identify new vulnerabilities. The models are available through Hugging Face and deployment partners including Microsoft, AWS, Cloudflare, and Ollama. Microsoft is also offering a GPU-optimised version of gpt-oss-20b for Windows via the AI Toolkit for Visual Studio Code. OpenAI also released reference implementations in PyTorch and Rust, a new tokeniser (o200k_harmony), and a harmony renderer to help developers integrate the models more easily. With this release, OpenAI re-enters the open-source AI race, not as a leader by size, but by signalling a broader shift in its approach to transparency, global competition, and developer trust. Whether the move will curb concerns or keep up with rapid developments elsewhere remains unclear.

Open AI Launches GPT-OSS: The First Open-Weight AI Model in 60 Years

Release of GPT-OSS Captures Interest of Developers, Researchers & Businesses Worldwide OpenAI has launched GPT-OSS, its first open-weight AI model after 60 years. The renowned AI startup the model earlier due to safety concerns. Users can now download GPT-OSS depending on their use case: a 120-billion-parameter model (top version) and a 20-billion-parameter model (lighter version). The 120-billion-parameter model can operate smoothly on a single Nvidia GPU, while the lighter variant only requires 16GB of memory to function. The models provide complete authority to the users over data and infrastructure. Additionally, they offer new and advanced options to . Both open-weight AI models are freely accessible under the Apache 2.0 license and can be accessed through platforms like Azure, AWS, Databricks, or Hugging Face. Moreover, these models can also be used commercially.

OpenAI releases open-weight reasoning models optimized for running on laptops

SAN FRANCISCO (Reuters) -OpenAI said on Tuesday it has released two open-weight language models that excel in advanced reasoning and are optimized to run on laptops with performance levels similar to its smaller proprietary reasoning models. An open-weight language model's trained parameters or weights are publicly accessible, which can be used by developers to analyze and fine-tune the model for specific tasks without requiring original training data. "One of the things that is unique about open models is that people can run them locally. People can run them behind their own firewall, on their own infrastructure," OpenAI co-founder Greg Brockman said in a press briefing. Open-weight language models are different from open-source models, which provide access to the complete source code, training data and methodologies. Separately, Amazon announced OpenAI's open-weight models are now available on its Bedrock generative AI marketplace in Amazon Web Services. It marks the first time an OpenAI model has been offered on Bedrock, said Atul Deo, Bedrock's director of product. "OpenAI has been developing great models and we believe that these models are going to be great open-source options, or open-weight model options for customers," said Deo, in an interview. He declined to discuss any contractual arrangements between AWS and OpenAI. Amazon shares tumbled last week after the company reported slowing growth in its AWS unit, particularly compared with rivals. The landscape of open-weight and open-source AI models has been highly contested this year. For a time, Meta's Llama models were considered the best, but that changed earlier this year when China's DeepSeek released a powerful and cost-effective reasoning model, while Meta struggled to deliver Llama 4. The two new OpenAI models are the first open models OpenAI has released since GPT-2, which was released in 2019. OpenAI's larger model, gpt-oss-120b, can run on a single GPU, and the second, gpt-oss-20b, is small enough to run directly on a personal computer, the company said. OpenAI said the models have similar performance to its proprietary reasoning models called o3-mini and o4-mini, and especially excel at coding, competition math and health-related queries. The models were trained on a text-only dataset which in addition to general knowledge, focused on science, math and coding knowledge. OpenAI did not release benchmarks comparing the open-weight models to competitors' models such as the DeepSeek-R1 model. Microsoft-backed OpenAI, currently valued at $300 billion, is currently raising up to $40 billion in a new funding round led by Softbank Group. (Reporting by Anna Tong and Greg Bensinger in San Francisco; Editing by Edwina Gibbs and Deepa Babington)

Meet GPT-OSS: OpenAI's 20B and 120B parameter open-weight models, the first in six years

Runs locally with support from NVIDIA, Qualcomm, Hugging Face, and Ollama, enabling private, fast, and customisable AI applications. For years, the world's most influential AI company stood on the sidelines of the open-source movement. But now, OpenAI is back in the game and this time, it's bringing heavyweight models to your laptop. With the release of GPT-OSS, OpenAI is offering its first open-weight large language models in over six years. The move signals a strategic pivot and also a quiet acknowledgment that openness and accessibility are now table stakes in the generative AI race. Available in two sizes, GPT-OSS-20B and GPT-OSS-120B, the models can be freely downloaded, modified, and even deployed commercially under the permissive Apache 2.0 licence. More strikingly, they're designed to run locally, even on consumer-grade NVIDIA GPUs or Snapdragon-powered mobile devices. So, what's really behind OpenAI's decision to open its models now? And how do GPT-OSS models compare in a crowded field filled with Llama, Gemma, DeepSeek, and Mistral? OpenAI's reluctance to open its model weights has always sat uncomfortably with its name. Once a lab that championed transparency, it became increasingly closed after the release of GPT-2, citing safety concerns. GPT-3, GPT-4, and even the most recent o4-mini models have been tightly controlled, hosted solely on OpenAI's own infrastructure and accessed via APIs. But the AI landscape has shifted. Over the last 18 months, developers and enterprises alike have flocked to open-weight alternatives. Meta's Llama family, Mistral's lean models, and challengers like DeepSeek and Falcon have thrived because they were decent and because they were open. You could inspect the architecture, fine-tune them for niche use cases, and even run them on consumer-grade hardware. More importantly, you weren't locked into a single provider's cloud billing or API usage policies. In January 2025, OpenAI CEO Sam Altman publicly admitted that the company had "been on the wrong side of history" by not participating in the open-weight ecosystem. With GPT-OSS, OpenAI is now trying to rewrite its role in the story. Also read: Did Sam Altman just tease GPT-5? OpenAI CEO's post fuels speculation The GPT-OSS models come in two flavours, GPT-OSS-20B and GPT-OSS-120B. Both variants support up to 131,072 token context lengths, making them ideal for large document processing, research tasks, and long-form conversations. OpenAI has confirmed that both models use a mixture-of-experts (MoE) architecture, which lets them dynamically adjust reasoning effort and compute based on prompt complexity. GPT-OSS-20B: A compact, chain-of-thought reasoning model that can run locally on just 16GB of memory. Ideal for mobile inference or slim desktop use cases. GPT-OSS-120B: A larger, more powerful model that matches the performance of OpenAI's own o4-mini. While it still requires a decent GPU (24GB+ VRAM recommended), it's designed to be deployed on a single NVIDIA GPU. Internally, these models perform on par with OpenAI's closed models on tasks such as coding, question answering, and common sense reasoning. Unlike GPT-4, however, these models are text-only, with no built-in multimodal capabilities. At least not yet. Open-weight models aren't just about ideology but they offer tangible benefits.Developers and organisations now have the freedom to run models locally, significantly improving data privacy while reducing latency. They can also fine-tune the models using their own proprietary datasets, bypassing the need to send sensitive information to external cloud providers. Beyond that, full access to the model weights allows for deeper inspection and auditing, which can be especially valuable in high-stakes applications like healthcare or finance. With these models, users can tailor inference behaviour, prompting strategies, and memory management to match their unique workflows, making AI integration far more flexible. OpenAI has acknowledged that a significant portion of its customer base already uses open models, and said this release is intended to address that demand by making its own technology more accessible across use cases. The sentiment was echoed by Sam Altman, who described the models as "the best and most usable open model in the world," designed to empower individuals and small businesses alike. Also read: OpenAI is crazy rich: ChatGPT maker earns $23,000 per minute OpenAI is also keen to reassure critics that GPT-OSS isn't a hasty pivot. The models have undergone what the company claims is its most rigorous safety testing to date, including evaluations by external security firms for potential misuse in areas like biosecurity and cybercrime. One of the standout features is the inclusion of visible chain-of-thought reasoning. This allows users to view how the model arrived at a specific conclusion or output, with the intent of making it easier to detect hallucinations, bias, or deceptive behaviour. This could also serve as a valuable tool for researchers studying model alignment or prompt engineering. OpenAI hasn't disclosed the exact training data sources, continuing its trend of opacity in that department. But the company insists that the models match internal safety benchmarks used for their proprietary offerings. While the ability to run GPT-OSS models in the cloud is valuable, the most exciting development might be how well these models perform on consumer-grade devices. In collaboration with Qualcomm, the smaller 20B model can now run entirely on-device using the Snapdragon AI Engine. This makes it possible for smartphones and tablets to perform chain-of-thought reasoning without ever hitting the cloud. Qualcomm called the move a "turning point" in mobile AI, and suggested this was only the beginning. For developers looking to deploy apps with built-in reasoning or contextual chat agents, this unlocks powerful new use cases in personal productivity, healthcare, assistive tech, and beyond. The tight integration with platforms like Ollama makes deployment significantly easier. On the PC side, NVIDIA has worked closely with OpenAI to optimise the GPT-OSS models for its RTX GPU lineup. If you're running an RTX 4080, 4090, or the newer 5090, the 120B model can be run locally at impressive speeds, reportedly up to 256 tokens per second on a 5090. That's fast enough for real-time applications like chat agents, coding assistants, or document summarisation. Developers can load the models via Ollama, llama.cpp, or Microsoft AI Foundry Local, with out-of-the-box support for RTX-specific optimisations like MXFP4 and TensorRT coming soon. According to NVIDIA, these models are also some of the first to support MXFP4, a mixed-precision format that delivers high-quality outputs with reduced compute overhead. The long context length (up to 131k tokens) is a game-changer for workflows that require deep reasoning over large datasets, be it legal documents, codebases, or academic literature. Also read: Study mode in ChatGPT explained: How students can use AI more effectively Getting started with GPT-OSS is refreshingly straightforward. OpenAI has made the models available across multiple platforms. On Hugging Face, both the 20B and 120B variants can be downloaded directly, complete with inference scripts and documentation. For those looking for a user-friendly interface, the Ollama app supports local deployment on Windows, macOS, and Linux, and includes SDKs for integration into other applications. Microsoft's AI Foundry Local caters to Windows developers, offering a command line interface and API integration powered by ONNX Runtime. For teams running cloud infrastructure, both Azure and AWS offer hosted deployment options, and Databricks is supporting the models for enterprise-scale workloads. Whether you're a hobbyist testing on a single machine or a startup looking to deploy AI across products, GPT-OSS is now accessible at nearly every level of technical maturity. OpenAI hasn't officially published benchmark comparisons between GPT-OSS and competing open models like Llama 3, DeepSeek-V2, or Gemma 2. But early community evaluations suggest the 120B model sits firmly in the same ballpark as Meta's Llama 3 70B and Google's Gemma 2 27B in terms of reasoning quality and instruction-following capabilities. We also looked at some of the recently benchmarked open-source models to create a comparison of sorts. The inclusion of MoE and high context length support gives GPT-OSS some clear technical advantages. And while it's not yet multimodal like GPT-4o, the focus on visible reasoning and local inference makes it more accessible than most of its rivals. That said, custom fine-tunes, alignment tuning, and quantised variants from the open community will likely determine how widely GPT-OSS gets adopted in the long run. OpenAI's decision to release GPT-OSS is a cultural shift. By joining the ranks of open-weight model providers, the company is effectively acknowledging that the centre of gravity in AI development has moved. Innovation is no longer restricted to research labs or big cloud providers. It's happening in home offices, small studios, and solo dev setups. This release also marks a return to OpenAI's original mission: to ensure that artificial general intelligence (AGI) benefits all of humanity. What stands to be seen is if they'll continue releasing open weights. Altman summed it up in a post: "We believe in individual empowerment. Although we believe most people will want to use a convenient service like ChatGPT, people should be able to directly control and modify their own AI when they need to, and the privacy benefits are obvious." Whether GPT-OSS becomes the most widely used open model remains to be seen. But it's clear that the days of AI being locked away behind proprietary APIs are numbered. Also, it's tempting to view GPT-OSS as a catch-up move, a response to pressure from Meta, Mistral, and the broader open-source community. But that would be missing the point. GPT-OSS is an impressive release on its own merits. The models are fast, flexible, and finely tuned for reasoning. They run locally. They're easy to deploy. And they carry the engineering polish you'd expect from OpenAI. More importantly, they signal a broader evolution in how AI will be built, deployed, and experienced. OpenAI is now a contributor to the open ecosystem it once watched from afar.

OpenAI releases GPT-OSS, its first open-weight AI model in over six years: What it is and what it can do

External safety audits and visible reasoning chains aim to ensure responsible usage. ChatGPT maker and a renowned AI startup OpenAI, after over six years has introduced its first open-weight AI model, GPT-OSS. The company has long avoided releasing such models due to safety concerns. However, the model is now available in two versions: a 120-billion-parameter model that can run on a single Nvidia GPU, and a lighter 20-billion-parameter variant optimised for systems with only 16GB of memory. The company stated that the larger model performs similarly to its o4-mini closed model, while the smaller model matches the performance of the o3-mini. Both models are released under the permissive Apache 2.0 license through platforms such as Hugging Face, Azure, AWS, and Databricks, making them freely available for commercial use and customisation. With this, OpenAI positions itself to compete with the growing ecosystem of open-source models, including Meta's Llama, DeepSeek, and Google's Gemma. Both models can reason, code, browse the web, and run agents using OpenAI APIs. However, the company has not disclosed the training data, but does state that it is the most thoroughly tested model yet. It also stated that external safety firms were brought in to audit the model for potential abuse in areas such as cybersecurity and biohazards. GPT-OSS also includes visible "chain-of-thought" reasoning, which is intended to help users trace how conclusions are reached and identify potential issues. The company has not provided some performance benchmarks against competing models, but claims that both GPT-OSS versions perform competitively on tasks such as code generation and reasoning tests. Furthermore, OpenAI has not set a timeline for future GPT-OSS updates; rather, the company views this release as a starting point for developers and businesses seeking greater control over how their data is used.