Introducing OpenAI o1

# Introducing OpenAI o1 Source: [https://openai.com/index/introducing-openai-o1-preview/](https://openai.com/index/introducing-openai-o1-preview/) We've developed a new series of AI models designed to spend more time thinking before they respond\. They can reason through complex tasks and solve harder problems than previous models in science, coding, and math\. Today, we are releasing the first of this series in ChatGPT and our API\. This is a preview and we expect regular updates and improvements\. Alongside this release, we’re also including [evaluations](https://openai.com/index/learning-to-reason-with-llms/)for the next update, currently in development\. We trained these models to spend more time thinking through problems before they respond, much like a person would\. Through training, they learn to refine their thinking process, try different strategies, and recognize their mistakes\. In our tests, the next model update performs similarly to PhD students on challenging benchmark tasks in physics, chemistry, and biology\. We also found that it excels in math and coding\. In a qualifying exam for the International Mathematics Olympiad \(IMO\), GPT‑4o correctly solved only 13% of problems, while the reasoning model scored 83%\. Their coding abilities were evaluated in contests and reached the 89th percentile in Codeforces competitions\. You can read more about this in our[technical research post](https://openai.com/index/learning-to-reason-with-llms/)\. As an early model, it doesn't yet have many of the features that make ChatGPT useful, like browsing the web for information and uploading files and images\. For many common cases GPT‑4o will be more capable in the near term\. But for complex reasoning tasks this is a significant advancement and represents a new level of AI capability\. Given this, we are resetting the counter back to 1 and naming this series OpenAI o1\. These enhanced reasoning capabilities may be particularly useful if you’re tackling complex problems in science, coding, math, and similar fields\. For example, o1 can be used by healthcare researchers to annotate cell sequencing data, by physicists to generate complicated mathematical formulas needed for quantum optics, and by developers in all fields to build and execute multi\-step workflows\. The o1 series excels at accurately generating and debugging complex code\. To offer a more efficient solution for developers, we’re also releasing[OpenAI o1‑mini](https://openai.com/index/openai-o1-mini-advancing-cost-efficient-reasoning/),a faster, cheaper reasoning model that is particularly effective at coding\. As a smaller model, o1‑mini is 80% cheaper than o1‑preview, making it a powerful, cost\-effective model for applications that require reasoning but not broad world knowledge\.