Introducing OpenAI o1

As part of developing these new models, we have come up with a new safety training approach that harnesses their reasoning capabilities to make them adhere to safety and alignment guidelines. By being able to reason about our safety rules in context, it can apply them more effectively. 

One way we measure safety is by testing how well our model continues to follow its safety rules if a user tries to bypass them (known as "jailbreaking"). On one of our hardest jailbreaking tests, GPT‑4o scored 22 (on a scale of 0-100) while our o1‑preview model scored 84. You can read more about this in the system card and our research post.

To match the new capabilities of these models, we’ve bolstered our safety work, internal governance, and federal government collaboration. This includes rigorous testing and evaluations using our Preparedness Framework⁠(opens in a new window), best-in-class red teaming, and board-level review processes, including by our Safety & Security Committee.

To advance our commitment to AI safety, we recently formalized agreements with the U.S. and U.K. AI Safety Institutes. We've begun operationalizing these agreements, including granting the institutes early access to a research version of this model. This was an important first step in our partnership, helping to establish a process for research, evaluation, and testing of future models prior to and following their public release.

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, 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.