OpenAI launches GPT-5.4 mini and nano models built for speed over raw power

OpenAI's Latest AI Models Are Built for Speed

Expertise Artificial intelligence, home energy, heating and cooling, home technology. The latest models for ChatGPT users and developers using OpenAI's API are designed to be workhorses, built for tasks like vibe coding, where big, powerful AI models are expensive overkill. GPT-5.4 mini and nano, which OpenAI released Tuesday, are the smallest and fastest versions of GPT-5.4, which debuted earlier this month. These new models are part of an effort by OpenAI to lean into coding as it battles with rival Anthropic for the AI software engineering market. OpenAI's latest models for its Codex coding software have directly challenged Anthropic's Claude Code, which went viral at the end of 2025 for its ability to create apps from scratch. Disclosure: Ziff Davis, CNET's parent company, in 2025 filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems. OpenAI says GPT-5.4 mini is more than twice as fast as its predecessor, GPT-5 mini, at tasks like coding, reasoning and tool use. On some benchmarks, it's close to the capabilities of the standard GPT-5.4 model. Read more: Best AI Chatbots of 2026 The company suggested GPT-5.4 mini is best for things like editing and debugging code. It could be used as a subagent in Codex, with a larger model like GPT-5.4 delegating certain tasks to the faster, cheaper model. GPT-5.4 nano is even smaller, and OpenAI suggested it for grunt work like classifying and extracting data. GPT-5.4 mini will be available for developers through the API and through Codex and ChatGPT. ChatGPT Free and Go users can access it through the "Thinking" feature. Others will find it as the fallback model when they hit the rate limit for GPT-5.4 Thinking. GPT-5.4 nano is only available in the API.

OpenAI's GPT-5.4 mini and nano launch - with near flagship performance at much lower cost

Developers can mix large planning models with cheaper subagents. Over the past few weeks, we have seen the generation of OpenAI's flagship large language models iterate from GPT-5.3 to GPT-5.4. Think of the model as the engine that powers AI computation. Each generational jump usually results in increased performance and accuracy. Also: OpenAI's new GPT-5.4 clobbers humans on pro-level work in tests - by 83% The actual releases can be a bit difficult to track without a scorecard. On March 5, OpenAI released GPT-5.4 Thinking, a high-performance, in-depth thinking model. Two days earlier, it released GPT-5.3 (not 5.4) Instant, a model that "makes everyday conversations more consistently helpful and fluid," but not necessarily more accurate. This week, OpenAI is releasing the GPT-5.4 mini and GPT-5.4 nano models. These models are designed for fast, efficient, high-volume AI workloads. These are basically the budget language model offerings. For many AI workflows, the most effective model is one that balances strong performance with fast responses and reliable tool use. According to OpenAI, "These models are built for the kinds of workloads where latency directly shapes the product experience: coding assistants that need to feel responsive, subagents that quickly complete supporting tasks, computer-using systems that capture and interpret screenshots, and multimodal applications that can reason over images in real-time." Also: Nvidia's 'ChatGPT moment' for self-driving cars, and other key AI announcements at GTC 2026 The company said, "In these settings, the best model is often not the largest one -- it's the one that can respond quickly, use tools reliably, and still perform well on complex professional tasks." Compared to GPT-5 mini, GPT-5.4 mini improves across coding, reasoning, multimodal understanding, and tool use. The model runs more than twice as fast as GPT-5 mini. GPT-5.4 nano is the smallest and fastest model, aimed at classification, extraction, ranking, and simpler coding-support tasks. When looking at the smaller, less expensive models, performance is the distinguishing factor. Buyers want to know just how much bang for the buck they're getting. To illustrate this performance, OpenAI is showing substantial benefits over models released just months earlier: GPT-5.4 mini approaches GPT-5.4-level pass rates while delivering faster execution. In other words, the smaller, lighter GPT-5.4 mini model performs almost as well as the full GPT-5.4 model on benchmark tests (the "pass rates") that measure if the model solves problems correctly. Also: Why encrypted backups may fail in an AI-driven ransomware era GPT-5.4 nano splits the difference. For example, it scores 52.39% on SWE-bench Pro and 46.30% on Terminal Bench 2.0, not as high as GPT-5.4 mini but still considerably better than GPT-5 mini. Technology specialist Hebbia builds tools that help professionals dig through enormous collections of documents using natural language. Their offerings appeal to users in sectors such as finance, law, and research, where the ability to analyze and derive insights from many documents at once is particularly helpful. According to Aabhas Sharma, CTO at Hebbia: "GPT-5.4 mini delivers strong end-to-end performance for a model in this class. In our evaluations, it matched or exceeded competitive models on several output tasks and citation recall at a much lower cost. It also achieved higher end-to-end pass rates and stronger source attribution than the larger GPT-5.4 model." Digital workspace Notion is the darling of internet-based productivity wonks. I'm writing this article in my Notion workspace. The technology provides a home for both structured and unstructured data. You can also use Notion to build no-code mini applications for information management. I use Notion to track my article production, internal projects, video plans, development projects, and more. Also: As AI agents spread, 1Password's new tool tackles a rising security threat Abhisek Modi, AI engineering lead at Notion, said: "GPT-5.4 mini handles focused, well-defined tasks with impressive precision. For editing pages specifically, it matched and often exceeded GPT-5.2 on handling complex formatting at a fraction of the compute." Modi continued: "Until recently, only the most expensive models could reliably navigate agentic tool calling. Today, smaller models like GPT-5.4 mini and nano can easily handle it, which will let our users build Custom Agents on Notion pick exactly the amount of intelligence they need." I haven't been super-impressed by Notion's AI. Hopefully, by incorporating these new models, Notion AI's performance will improve considerably. When you start to look at how agents fit into the overall ecosystem, it becomes apparent that AI can be structured to mirror real-world human operations. For example, you can combine a more powerful AI model (like GPT-5.4 Thinking) with faster, cheaper models like GPT-5.4 mini in the same way you might have a senior engineer managing a team of junior engineers. Also: Nvidia wants to own your AI data center from end to end Agentic systems can combine models of different sizes, with larger models planning tasks and smaller models executing subtasks. In this context, GPT-5.4 mini can handle subagent work, such as searching codebases, reviewing files, and processing documents. OpenAI said: "GPT-5.4 mini is also strong on multimodal tasks, particularly those related to computer use. The model can quickly interpret screenshots of dense user interfaces to complete computer use tasks with speed." GPT-5.4 mini is available in API, Codex, and ChatGPT versions. For Free and Go tier users, GPT-5.4 mini is accessible via the "Thinking" option in the plus menu. OpenAI said: "For all other users, GPT-5.4 mini is available as a rate limit fallback for GPT-5.4 Thinking." Also: I used GPT-5.2-Codex to find a mystery bug and hosting nightmare - it was beyond fast The company said that for programmers, GPT-5.4 mini is available across the Codex app, CLI, IDE extension, and web. OpenAI said that the mini model "Uses only 30% of the GPT-5.4 quota, letting developers quickly handle simpler coding tasks in Codex for about one-third the cost." Additionally, Codex can also delegate to GPT-5.4 mini subagents so that less reasoning-intensive work runs on the less costly model. You can see how costs compare when you look at them side by side: By comparison, GPT-5.4 is priced at $2.50 per million input tokens and $15.00 per million output tokens. That's a lot more expensive. It makes sense that if you're trying to keep costs down and don't need the extra processing power, it's better to use the mini and nano models. Have you experimented with smaller AI models, like GPT-5.4 mini or nano, in your own workflows? Do you prefer using the largest models available, or do you find faster, cheaper models are often "good enough" for real-time tasks like coding, document analysis, or agent workflows? If you build AI-powered tools, how do you decide when to use a full reasoning model versus a lightweight subagent model? Let us know what you're seeing in practice and comment below.

GPT-5.4 mini brings some of the smarts of OpenAI's latest model to ChatGPT Free and Go users

When OpenAI released GPT-5.4 at the start of March, the company said the new model was designed primarily for professional work like programming and data analysis. Now OpenAI is launching GPT-5.4 mini and nano, and while it is once again highlighting the usefulness of these new systems for tasks like coding, one of the new models is available to Free and Go users. What's more, that model, GPT-5.4 mini, even offers performance that approaches GPT-5.4 in a handful of areas. As a Free or Go user, you can access 5.4 mini by selecting "Thinking" from ChatGPT's plus menu. For paid users, the model is the new fallback for when you've hit your rate limit with 5.4 proper. OpenAI says 5.4 mini offers better performance than GPT-5.0 mini in a few different key areas, including reasoning, multimodal understanding and tool use. That means 5.4 mini is better at parsing non-text inputs such as images and audio, and has a more nuanced understanding of how to do things like search the web. It does all of this while running more than twice as fast as its predecessor. As for GPT-5.4 nano, OpenAI says it's ideal for tasks such as data classification and extraction where speed and cost-efficiency are top of mind. If you're a ChatGPT user, you won't find the new model in the chatbot. Instead, OpenAI is making it only available through its API service. The company envisions developers using more advanced models to delegate tasks to AI agents running GPT-5.4 nano, and that's reflected in the cost of the new model, which OpenAI has priced starting at $0.20 per million input tokens.

You probably won't use OpenAI's newest model -- but it's about to change every AI tool you use

The new models could transform how apps and AI tools work behind the scenes The other day I got a text from a developer friend of mine and we chatted for a minute about how AI launches have started to feel predictable. Bigger models, better benchmarks and incremental upgrades that don't always change how these tools actually work. This friend said, "I just want newer versions of the smaller models!" Luckily, if you feel that way, too, OpenAI's latest release has broken a pattern. Yesterday, OpenAI introduced GPT-5.4 mini and GPT-5.4 nano, two new models designed to be faster, more efficient and better suited for high-volume tasks. On the surface, they might sound like scaled-down versions of something bigger. In reality though, they point to a much bigger shift. The focus has shifted from power to speed. Similarly, Google recently released Gemini 3 flash-lite -- a smaller model with improved speed. Move over power, users want speed GPT-5.4 mini is a significant upgrade over GPT-5 mini, with improvements across coding, reasoning, multimodal understanding and tool use -- while running more than twice as fast. It also approaches GPT-5.4 on several evaluations, including coding-focused benchmarks like SWE-Bench and OSWorld-Verified. For those keeping track, Anthropic's Opus 4.6 still tops the benchmarks. But it's the combination -- near-flagship performance with much faster response times -- where things get interesting. Because for most people, the best AI isn't the smartest one. I know that may sound surprising but most AI users want a model that responds instantly that can fit into their workflow. Think of it this way, you don't need a rocket scientist helping with your summarization or content calendar for social media. A brain of average intelligence that gets the job done fast helps with productivity most in this case. So, that's the difference between something you try once and something you actually use every day. The real shift is happening behind the scenes The real shift is happening behind the scenes. How these models are meant to be used is already changing the trajectory of AI. Specifically, "subagents" -- smaller models like GPT-5.4 mini running in parallel, each handling a specific task while a larger model oversees the bigger picture. In other words, instead of one model doing everything, AI systems are starting to look more like teams where a powerful model handles planning and coordination while smaller models execute tasks quickly and multiple processes run at the same time in the background. It's a more efficient way to work -- and it's how many modern AI tools are already starting to evolve. And yet, you may never use this model directly. Most people won't ever choose GPT-5.4 mini or nano from a dropdown. But they'll absolutely notice the impact. These smaller models are designed to power: * faster responses inside apps * real-time assistants that don't lag * background tasks like summarizing, ranking and extracting data * AI tools that feel more responsive and less like they're "thinking" GPT-5.4 nano, in particular, is built for high-throughput tasks -- the kind of invisible work that supports everything from search results to smart features inside apps. For a long time, AI development focused on making a single model as powerful as possible. But that's starting to change. Instead, we're seeing a shift toward systems where different models handle different parts of a task -- all working together at once. That leads to faster outputs, more consistent performance and tools that feel smoother and more reliable. Essentially, AI is becoming less about one big brain -- and more about coordinated systems that get things done faster. Bottom line The new models are available in the drop down menu now. Give them a try and see what you think. GPT-5.4 mini and nano are more efficient and soon to be more distributed and increasingly invisible. And as these models start powering the tools people already use, the biggest change won't be what AI can do. Instead, it'll be how seamlessly AI does it to the point of not even noticing. Follow Tom's Guide on Google News and add us as a preferred source to get our up-to-date news, analysis, and reviews in your feeds.

OpenAI shrinks GPT-5.4 for speed and lower costs

New mini and nano models give developers faster AI at a fraction of the price. OpenAI is scaling its latest models down to hit a different target, faster responses and much lower costs. The new GPT-5.4 mini and nano are built for developers who care more about responsiveness than squeezing out every last bit of reasoning power. Both models are available starting today. GPT-5.4 mini runs more than twice as fast as its predecessor while staying close to the full GPT-5.4 on key benchmarks. GPT-5.4 nano takes that further, focusing on simpler tasks like classification and data extraction where efficiency matters most. Recommended Videos This approach fits apps where speed shapes the experience. Coding assistants, background agents, and real-time vision tools depend on quick feedback, and in those cases a slightly smaller model often delivers a better overall result. How much performance you actually lose The performance gap between models is narrower than you might expect. GPT-5.4 mini scores 54.4 percent on SWE-Bench Pro, compared to 57.7 percent for the full model. On OSWorld-Verified, the mini reaches 72.1 percent while the larger version hits 75 percent, keeping the difference tight across tasks. Costs drop far more dramatically. GPT-5.4 mini is priced at $0.75 per million input tokens and $4.50 per million output tokens, while nano comes in at $0.20 and $1.25. Both models support text and image inputs, tool use, function calling, and a 400,000 token context window, so the lower price doesn't strip away core capabilities. In Codex, the mini model uses just 30 percent of the GPT-5.4 quota. That lets developers shift routine coding work to a cheaper tier while saving the full model for harder reasoning. When smaller models do the heavy lifting OpenAI is also pushing a multi-model workflow. Instead of relying on one system, developers can split work across tiers, pairing a larger model for planning with smaller ones handling execution. That setup reflects how many real apps already behave. One model can review a codebase or decide on changes, while another processes supporting data or repetitive steps. The smaller model handles the predictable work, while the larger one focuses on judgment and coordination. Early feedback suggests this mix is effective. Hebbia CTO Aabhas Sharma reported that GPT-5.4 mini matched or outperformed competing models on several tasks at a lower cost, and in some cases even delivered stronger end-to-end results than the full GPT-5.4. What to use and when GPT-5.4 mini is now available across the API, Codex, and ChatGPT. Free and Go users can access it through the Thinking option, while other users may see it as a fallback when they hit limits on GPT-5.4 Thinking. The nano model is currently limited to the API, aimed at teams running high-volume workloads where cost control is critical. Both models are live today with full documentation available. For developers building real-time AI features, the shift is clear. Smaller models are now capable enough to handle a larger share of everyday work, which makes choosing the right balance of speed, cost, and capability an increasingly practical decision.

OpenAI Releases GPT-5.4 Mini and Nano, Which Could Be More Useful Than the Big Model

Developers can now run hybrid AI systems where a flagship model plans tasks while smaller models handle the bulk of the work. OpenAI isn't slowing down. Less than two weeks after launching GPT-5.4 -- itself released just two days after GPT-5.3 -- the company dropped two more models on Tuesday: GPT-5.4 Mini and GPT-5.4 Nano. These aren't stripped-down versions of the flagship model -- they're purpose-built machines designed for the kind of work where waiting half a minute for an answer is not an option. OpenAI calls them its "most capable small models yet," saying that GPT-5.4 Mini is more than two times faster than GPT-5 Mini. If you've ever watched a coding assistant think for 45 seconds before editing three lines of code, then you understand the appeal of a fast model. So why would anyone release a less accurate model on purpose? The short answer: because accuracy isn't always the bottleneck. If you're running a customer service chatbot that answers the same 200 questions all day, then you don't need the model that scored best on PhD-level chemistry exams. You need the one that responds in under a second and costs a fraction of a cent per reply. That's the space these models are built for. But it doesn't mean these models are dumb or unreliable. On coding benchmarks, GPT-5.4 Mini scored 54.4% on SWE-Bench Pro -- a test that measures a model's ability to fix real GitHub issues -- compared to 45.7% for the old GPT-5 Mini and 57.7% for the full GPT-5.4. On OSWorld-Verified, which tests how well a model can actually operate a desktop computer by reading screenshots, Mini hit 72.1%, just shy of the flagship's 75.0% -- and both clear the human baseline of 72.4%. GPT-5.4 Nano, meanwhile, scores 52.4% on SWE-Bench Pro and 39.0% on OSWorld -- lower than Mini, but still a major leap over previous Nano-class models. "GPT-5.4 marks a step forward for both Mini and Nano models in our internal evaluations," Perplexity Deputy CTO Jerry Ma said after testing both. "Mini delivers strong reasoning, while Nano is responsive and efficient for live conversational workflows." Instead of routing every single task through an expensive flagship model, you can now build systems where the big model plans and coordinates while smaller models handle the actual grunt work in parallel -- searching a codebase here, reading a document there, or processing a form somewhere else. As we saw in our GPT-5.4 vs. Grok 4.20 comparison, where the model sits in the workflow matters as much as which model you pick. GPT-5.4 Mini runs at a rate of $0.75 per million input tokens and $4.50 per million output tokens via the API. GPT-5.4 Nano is even cheaper: $0.20 per million input tokens and $1.25 per million output tokens -- a price point that makes running a huge amount of queries per day financially realistic for startups. For context, Nano is roughly four times cheaper than Mini on inputs. For regular ChatGPT users, GPT-5.4 Mini is available today to Free and Go users via the "Thinking" option in the plus menu. Paid subscribers who hit their GPT-5.4 rate limits will automatically fall back to Mini. GPT-5.4 Nano, however, is API-only for now -- OpenAI is clearly positioning it as a developer tool, not a consumer one.

OpenAI releases GPT-5.4 mini and nano models

OpenAI has launched GPT-5.4 mini and nano models, expanding the capabilities of its GPT-5.4 system originally released in March 2023. The GPT-5.4 mini model is now available to Free and Go users, allowing them to utilize advanced features previously accessible only to paid subscribers. This model provides performance that approaches the original GPT-5.4 in several key areas, including reasoning and multimodal understanding. The mini version can be accessed by selecting "Thinking" from ChatGPT's plus menu for Free and Go users. For those with paid subscriptions, GPT-5.4 mini will serve as a fallback option when they reach their rate limit with the full GPT-5.4 model. OpenAI highlighted improvements in GPT-5.4 mini over its predecessor, GPT-5.0 mini, particularly in effectively interpreting non-text inputs like images and audio. It also features enhanced web-searching capabilities and operates more than twice as fast as GPT-5.0 mini. In addition to GPT-5.4 mini, OpenAI has introduced the GPT-5.4 nano model, which targets speed and cost-efficiency for specific tasks like data classification and extraction. Unlike the mini version, GPT-5.4 nano is available exclusively through OpenAI's API service and not within the chatbot interface. According to OpenAI, developers can utilize this model for delegating tasks to AI agents, with costs starting at $0.20 per million input tokens.

OpenAI Just Revealed Cheaper Versions of Its Flagship Model. Here's How to Use Them

OpenAI has introduced GPT-5.4 mini and GPT-5.4 nano, two small, cost-efficient versions of its flagship AI model. In a press release, OpenAI said that 5.4 nano and mini are "our most capable small models yet," coming close to matching the default GPT-5.4 model's abilities at coding and agentically operating software. But they come at a much cheaper price. The new models are also significantly faster than the flagship model and previous small models, making them particularly useful for collaborative vibe coding work. OpenAI typically releases models in four sizes; a large pro version, a middle-of-the-road standard version, a small mini version, and an even smaller nano version. Smaller models are faster and cheaper, but not as capable as their larger siblings. Both models excel in "workloads where latency directly shapes the product experience," according to OpenAI, such as coding assistants that can operate and make changes in real time and computer-use agents that can handle data entry.

OpenAI's Faster GPT-5.4 Mini and Nano AI Models Are Here: Details

OpenAI says GPT-5.4 mini is also capable of handling subagent tasks OpenAI introduced two new artificial intelligence (AI) models in the GPT-5.4 family on Tuesday. Dubbed GPT-5.4 mini and GPT-5.4 nano, the two smaller AI models are faster compared to the larger models in the family, and are aimed at low-latency workloads. Some of the key strengths of these models include coding proficiency, computer use, multimodal understanding, and subagent handling. For developers, these models will also be cost-efficient, given the lower cost of input and output tokens. OpenAI Introduces GPT-5.4 Mini and GPT-5.4 Nano In a blog post, the San Francisco-based AI giant announced the release of the two new models. GPT‑5.4 mini is now available via the application programming interface (API), Codex, and ChatGPT. In the API, the model supports text and image inputs, tool use, function calling, web and file search, computer use, and skills with its 400,000 tokens context window. It costs $0.75 per million input tokens and $4.50 per million output tokens. In the API, GPT‑5.4 mini supports text and image inputs, tool use, function calling, web search, file search, computer use, and skills. It has a 400k context window and costs $0.75 (roughly Rs. 68) per 1M input tokens and $4.50 (roughly Rs. 416) per 1M output tokens. Notably, GPT-5.4 mini is available to the free and Go tiers via the Thinking feature, whereas other tiers will find it as a fallback model after they hit the rate limit for GPT-5.4 Thinking. Coming to GPT-5.4 nano, it is currently only available as an API offering, with pricing set at $0.20 per million input and $1.25 per million output tokens. On capabilities, both models are optimised for coding-related tasks as long as they are deployed in fast, iterative environments. OpenAI says the models "handle targeted edits, codebase navigation, front-end generation, and debugging loops with low latency." Additionally, it is said that the 5.4 mini outperforms GPT-5-mini in most areas at similar latencies. Another unique strength of the model is subagent handling. While the larger AI models in the family are suitable for more complex agentic tasks involving planning, coordination, and final judgment, the mini variant can handle subagents that take care of narrower subtasks in parallel. OpenAI says these smaller models offer developers the option to compose systems where one single model is not overlooking every subtask in an agentic workflow. Apart from this, the company claims that the mini variant also excels on multimodal tasks around computer use. Interestingly, on the OSWorld-Verified benchmark, the mini variant approaches GPT-5.4.

OpenAI launches GPT-5.4 Mini and Nano models following GPT-5.4 debut - The Economic Times

The flurry of model releases comes as the generative AI space becomes more competitive. The company had been fielding tough competition from Google's Gemini models last year. The beginning of this year propelled Anthropic's models to the top of the charts, after the Claude maker stood up to the US government.Sam Altman's OpenAI announced the release of GPT-5.4 mini and GPT-5.4 nano on Wednesday, describing them as its most advanced compact AI models to date. The launch comes shortly after the company rolled out its GPT-5.4 AI model earlier this week. OpenAI has positioned GPT-5.4 as its leading model for professional workflows across its chatbot ChatGPT and coding platform Codex. The flurry of model releases comes as the generative AI space becomes more competitive. The company had been fielding tough competition from Google's Gemini models last year. The beginning of this year propelled Anthropic's models to the top of the charts, after the Claude maker stood up to the US government. The new model GPT-5.4 mini offers improvements over the GPT-5 mini in various areas, including coding, reasoning, multimodal tasks, and tool use, the company said in a statement. The model runs more than twice as fast while approaching GPT-5.4 performance levels on several benchmarks. GPT-5.4 nano, the smallest variant, targets simpler tasks such as classification, data extraction, ranking, and basic coding support. OpenAI has touted the new models for applications where response speed is key, such as coding assistants, subagents for supporting tasks, screenshot interpretation, and real-time image analysis. In internal benchmarks, GPT-5.4 mini reportedly outperforms GPT-5 mini at similar speeds, providing what the company calls a strong performance-to-latency balance. Within Codex, the new models support hybrid setups. GPT-5.4 can manage planning and oversight, while GPT-5.4 mini handles subtasks such as codebase searches or file reviews, the statement said. OpenAI notes this uses 30% of GPT-5.4 quotas at about one-third the cost.

OpenAI's ChatGPT 5.4 Mini & Nano Launch : Pricing & Benchmarks

OpenAI has introduced two new AI models, ChatGPT 5.4 Mini and ChatGPT 5.4 Nano, aimed at providing more accessible and cost-efficient options for developers and businesses. As highlighted by Universe of AI, these models are tailored for specific workloads where full-scale capabilities of GPT 5.4 may not be necessary. For instance, ChatGPT 5.4 Mini is designed to handle tasks like coding workflows and multimodal understanding with reduced resource consumption, while Nano focuses on high-volume, repetitive tasks such as data extraction and classification. Both models prioritize affordability, with Nano priced at just $0.20 per million input tokens, making it an attractive choice for budget-conscious applications. Explore how these models can optimize workflows in areas like customer service automation, coding assistance and sub-agent integration within larger AI systems. You'll gain insight into the performance benchmarks of Mini and Nano, including their respective strengths in efficiency and scalability. Additionally, learn how their pricing structure compares to GPT 5.4, offering significant savings for enterprises without compromising essential functionality. This preview provides a detailed breakdown of how these models can fit into diverse operational needs. The introduction of ChatGPT 5.4 Mini and Nano highlights OpenAI's strategic focus on creating AI tools optimized for specific workloads. These smaller models are particularly suited for applications where absolute precision is less critical, but speed and cost-effectiveness are essential. Common use cases include: By offering these tailored solutions, OpenAI enables businesses and developers to optimize their workflows while maintaining a balance between performance and cost. ChatGPT 5.4 Mini is designed to deliver a robust combination of performance and cost savings. It excels in tasks requiring moderate computational power, such as coding workflows, reasoning and multimodal understanding. Mini also supports agentic tool calling, a feature that enhances its utility in automation-heavy environments. Key performance metrics include: Despite its smaller size, Mini achieves results comparable to GPT 5.4 in many scenarios, making it a cost-effective alternative. For instance, in coding workflows, Mini can efficiently handle subtasks with low latency while consuming only 30% of GPT 5.4's resource quota. Priced at $0.75 per million input tokens and $4.50 per million output tokens, it offers significant savings for enterprises seeking high performance at a reduced cost. Advance your skills in ChatGPT 5 by reading more of our detailed content. ChatGPT 5.4 Nano is the most compact model in the lineup, optimized for high-volume, repetitive tasks such as classification, data extraction and ranking. While its performance is more modest, scoring 52.39% on the Software Engineering Bench Pro and 39% on OS World Verified, it is specifically designed for lightweight operations. Nano is ideal for developers managing extensive pipelines of simple tasks, where cost efficiency is a top priority. At $0.20 per million input tokens and $1.25 per million output tokens, Nano offers unparalleled affordability. Although it is not intended for complex reasoning or intricate problem-solving, its ability to handle large-scale workloads makes it an attractive option for businesses aiming to scale their operations without exceeding budget constraints. The pricing structure of Mini and Nano underscores OpenAI's commitment to affordability. Compared to the flagship GPT 5.4, which costs $2.50 per million input tokens and $15 per million output tokens, the smaller models provide substantial cost savings: These cost-effective options make advanced AI technology more accessible to a broader range of users, allowing businesses to scale their AI capabilities without compromising on quality or exceeding financial limitations. Enterprises have reported notable success with ChatGPT 5.4 Mini, particularly in workflows where cost efficiency and source attribution are critical. Both Mini and Nano now support agentic tool calling, a feature previously exclusive to larger models. This functionality enhances their effectiveness in automation-driven environments, where precision and scalability are essential. Mini is available across ChatGPT, Codex and OpenAI's API, providing developers with multiple integration options. Nano, on the other hand, is currently offered exclusively through the API, making sure that developers can select the model that best aligns with their specific requirements. The launch of ChatGPT 5.4 Mini and Nano represents a strategic evolution in OpenAI's approach to AI model architecture. By reserving the flagship GPT 5.4 for complex, high-stakes tasks and introducing smaller models for simpler workloads, OpenAI is reshaping how AI is deployed across industries. This tiered strategy not only enhances cost efficiency but also accelerates the adoption of AI technologies in diverse applications. The development of these models reflects a broader trend in the AI industry toward creating practical, scalable solutions. As businesses and developers continue to seek tools that balance performance, affordability and usability, the introduction of Mini and Nano sets a new standard for accessible and efficient AI deployment. Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

OpenAI Launches GPT-5.4 Mini & Nano to Power Faster, Lightweight AI

OpenAI Rolls Out GPT-5.4 Mini And Nano With Faster Performance, Lower Costs For Developers OpenAI has introduced two new artificial intelligence models, GPT-5.4 mini and GPT-5.4 nano, expanding its lineup of lightweight AI systems. The company describes them as its "most capable small models yet," designed to deliver strong performance while prioritizing speed, efficiency, and lower operating costs. The new models bring several capabilities of the larger GPT-5.4 system into a more compact format. They are aimed at real-time applications such as , automated workflows, and tools that process images quickly.

OpenAI launches GPT 5.4 mini and nano, its most capable small AI models yet: How to use them

In ChatGPT, GPT-5.4 mini is available to Free and Go users via the 'Thinking' option found in the plus menu. OpenAI has introduced two new AI models, GPT 5.4 mini and GPT 5.4 nano. According to OpenAI, these are the company's 'most capable small models yet.' The company says the new models bring many of the capabilities of the larger GPT 5.4 model but in a lighter format. They are designed for situations where speed and responsiveness are important, such as coding assistants, automated workflows, and applications that process images in real time. Among the two, GPT-5.4 mini is positioned as the more powerful option. It improves over the earlier GPT 5 mini model across several areas, including coding, reasoning, multimodal understanding, and tool usage. According to OpenAI, the model can also run more than twice as fast while still achieving performance close to the larger GPT 5.4 model in certain tests. Also read: Elon Musk's xAI faces lawsuit from minors alleging Grok created their explicit AI images The second model, GPT-5.4 nano, is the smallest and most affordable version in the GPT 5.4 family and is designed for tasks where speed and cost matter the most. OpenAI suggests using it for classification, data extraction, ranking, and coding subagents that handle simpler supporting tasks. GPT-5.4 mini can be accessed through API,ChatGPT and Codex. In the API, GPT-5.4 mini supports text and image inputs, tool use, function calling, web search, file search, computer use, and skills. It includes a 4,00,000-token context window and costs $0.75 per million input tokens and $4.50 per million output tokens. In Codex, GPT 5.4 mini is available across the Codex app, CLI, IDE extension, and web. It uses only 30 per cent of the GPT 5.4 quota, allowing developers to handle simpler coding tasks at roughly one-third of the cost. Also read: OpenAI declares code red, calls Anthropic's success a wake-up call In ChatGPT, GPT-5.4 mini is available to Free and Go users via the 'Thinking' option found in the plus menu. For other ChatGPT users, the model appears as a fallback option when the main GPT-5.4 Thinking model reaches its usage limit. Meanwhile, GPT-5.4 nano is currently available only through the API. It costs $0.20 per million input tokens and $1.25 per million output tokens.