OpenAI Unveils Enhanced Reasoning Transparency for o3-mini Model

OpenAI responds to DeepSeek competition with detailed reasoning traces for o3-mini

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More OpenAI is now showing more details of the reasoning process of o3-mini, its latest reasoning model. The change was announced on OpenAI's X account and comes as the AI lab is under increased pressure by DeepSeek-R1, a rival open model that fully displays its reasoning tokens. Models like o3 and R1 undergo a lengthy "chain of thought" (CoT) process in which they generate extra tokens to break down the problem, reason about and test different answers and reach a final solution. Previously, OpenAI's reasoning models hid their chain of thought and only produced a high-level overview of reasoning steps. This made it difficult for users and developers to understand the model's reasoning logic and change their instructions and prompts to steer it in the right direction. OpenAI considered chain of thought a competitive advantage and hid it to prevent rivals from copying to train their models. But with R1 and other open models showing their full reasoning trace, the lack of transparency becomes a disadvantage for OpenAI. The new version of o3-mini shows a more detailed version of CoT. Although we still don't see the raw tokens, it provides much more clarity on the reasoning process. Why it matters for applications In our previous experiments on o1 and R1, we found that o1 was slightly better at solving data analysis and reasoning problems. However, one of the key limitations was that there was no way to figure out why the model made mistakes -- and it often made mistakes when faced with messy real-world data obtained from the web. On the other hand, R1's chain of thought enabled us to troubleshoot the problems and change our prompts to improve reasoning. For example, in one of our experiments, both models failed to provide the correct answer. But thanks to R1's detailed chain of thought, we were able to find out that the problem was not with the model itself but with the retrieval stage that gathered information from the web. In other experiments, R1's chain of thought was able to provide us with hints when it failed to parse the information we provided it, while o1 only gave us a very rough overview of how it was formulating its response. We tested the new o3-mini model on a variant of a previous experiment we ran with o1. We provided the model with a text file containing prices of various stocks from January 2024 through January 2025. The file was noisy and unformatted, a mixture of plain text and HTML elements. We then asked the model to calculate the value of a portfolio that invested $140 in the Magnificent 7 stocks on the first day of each month from January 2024 to January 2025, distributed evenly across all stocks (we used the term "Mag 7" in the prompt to make it a bit more challenging). o3-mini's CoT was really helpful this time. First, the model reasoned about what the Mag 7 was, filtered the data to only keep the relevant stocks (to make the problem challenging, we added a few non-Mag 7 stocks to the data), calculated the monthly amount to invest in each stock, and made the final calculations to provide the correct answer (the portfolio would be worth around $2,200 at the latest time registered in the data we provided to the model). It will take a lot more testing to see the limits of the new chain of thought, since OpenAI is still hiding a lot of details. But in our vibe checks, it seems that the new format is much more useful. What it means for OpenAI When DeepSeek-R1 was released, it had three clear advantages over OpenAI's reasoning models: It was open, cheap and transparent. Since then, OpenAI has managed to shorten the gap. While o1 costs $60 per million output tokens, o3-mini costs just $4.40, while outperforming o1 on many reasoning benchmarks. R1 costs around $7 and $8 per million tokens on U.S. providers. (DeepSeek offers R1 at $2.19 per million tokens on its own servers, but many organizations will not be able to use it because it is hosted in China.) With the new change to the CoT output, OpenAI has managed to somewhat work around the transparency problem. It remains to be seen what OpenAI will do about open sourcing its models. Since its release, R1 has already been adapted, forked and hosted by many different labs and companies potentially making it the preferred reasoning model for enterprises. OpenAI CEO Sam Altman recently admitted that he was "on the wrong side of history" in open source debate. We'll have to see how this realization will manifest itself in OpenAI's future releases.

OpenAI now reveals more of its o3-mini model's thought process | TechCrunch

In response to pressure from rivals including Chinese AI company DeepSeek, OpenAI is changing the way its newest AI model, o3-mini, communicates its step-by-step "thought" process. On Thursday, OpenAI announced that free and paid users of ChatGPT, the company's AI-powered chatbot platform, will see an updated "chain of thought" that shows more of the model's "reasoning" steps and how it arrived at answers to questions. Subscribers to premium ChatGPT plans who use o3-mini in the "high reasoning" configuration will also see this updated readout, according to OpenAI. "We're introducing an updated [chain of thought] for o3-mini designed to make it easier for people to understand how the model thinks," an OpenAI spokesperson told TechCruch via email. "With this update, you will be able to follow the model's reasoning, giving you more clarity and confidence in its responses." Reasoning models like o3-mini thoroughly fact-check themselves before giving out results, which helps them to avoid some of the pitfalls that normally trip up models. The trade-off is that reasoning models take a little longer to arrive at solutions -- typically seconds to minutes longer. DeepSeek's R1 model, a "reasoning" model along the lines of o3-mini, reveals its full thought process, which many AI researchers argue is the preferred approach. In addition to making the model easier to study, the reasoning steps deliver a better user experience in certain situations, helping indicate when the model might be on the right -- or wrong -- track. OpenAI had opted not to show the full reasoning steps for o3-mini and its predecessors, o1 and o1-mini, in part due to competitive reasons. Instead, users only saw summaries of the reasoning steps -- summaries that were at times erroneous. OpenAI still isn't showing o3-mini's full reasoning steps, but the company said it "found a balance": o3-mini can "think freely" and then organize its "thoughts" into more detailed summaries. "To improve clarity and safety, we've added an additional post-processing step where the model reviews the raw chain of thought, removing any unsafe content, and then simplifies any complex ideas," the OpenAI spokesperson continued. "Additionally, this post-processing step enables non-English users to receive the chain of thought in their native language, creating a more accessible and friendly experience." In a Reddit AMA last week, Kevin Weil, OpenAI's chief product officer, hinted that the change was coming. "We're working on showing a bunch more than we show today -- [showing the model thought process] will be very, very soon," he said. "TBD on all -- showing all chain of thought leads to competitive distillation, but we also know people (at least power users) want it, so we'll find the right way to balance it."

OpenAI's o3-mini now lets you see the AI's thought process

OpenAI released its o3-mini model exactly one week ago, offering both free and paid users a more accurate, faster, and cheaper alternative to o1-mini. Now, OpenAI has updated the o3-mini to include an "updated chain of thought," and here's why it matters. OpenAI announced via an X post that free and paid users would now be able to view the reasoning process the o3-mini goes through before arriving at a conclusion. For example, in the post, a user asked, "How is today not a Friday?" and under the dropdown showing how long it took, the model delineated every step in its chain of thought that allowed it to land on its answer. Understanding how the model arrived at the conclusion is helpful because it not only helps users verify the accuracy of the conclusion, but it also teaches users how they could have arrived at that answer themselves. This is particularly useful for math or coding prompts, in which seeing the steps could allow you to recreate them the next time you encounter a similar problem. Also: OpenAI launches new o3-mini model - here's how free ChatGPT users can try it Paid ChatGPT subscribers will also be able to see the updated chain of thought in o3-mini in the "high reasoning" effort. As the name implies, "high reasoning" just allows the model to apply more compute power for more advanced questions that require higher reasoning. In the X post announcing the feature, OpenAI throws out the term "Chain of Thought," but what does it actually mean? In the same way you would ask a person to explain their reasoning step by step, CoT prompting encourages an LLM to break down a complex problem into logical, smaller, and solvable steps. By sharing these reasoning steps with users, the model becomes more interpretable, allowing users to better steer its responses and identify errors in reasoning. Also: OpenAI eyes the wearables business: Robots, headsets, watches and a whole lot more Raw CoT would display every intermediate step in real time as the model reasons through a problem. OpenAI's take on CoT in this update is not raw, as it is summarizing the reasoning for users. This has caused many AI aficionados in the comments of the X post to express discontent with the feature, as raw CoT poses added benefits, such as how to better steer the model and troubleshoot incorrect reasoning. Some reasons OpenAI could have chosen to go with its take on CoT are that it makes it easier for everyone to understand, and that exposing raw CoT could make the model more vulnerable to jailbreaking attempts. To view the chain of thought, you do not need to do anything other than select the o3-mini model to answer your prompt. If you are a subscriber, you can select "o3-mini" or "o3-mini-high" from the model toggle dropdown in the upper left-hand corner. Once it is selected, any prompt you enter will automatically show its reasoning process. Also: From zero to millions? How regular people are cashing in on AI If you are a free user, all you have to do is click on "Reason" in the message textbox or regenerate a response to activate o3-mini. Once you do, you can just enter a prompt as usual and see the magic for yourself.