Research

Advancing cost-efficient reasoning

We are introducing OpenAI o1, a new large language model trained with reinforcement learning to perform complex reasoning. o1 thinks before it answers—it can produce a long internal chain of thought before responding to the user.

We’re releasing a human-validated subset of SWE-bench that more reliably evaluates AI models’ ability to solve real-world software issues.

GPT-4o system card external testers acknowledgements

We’ve developed and applied a new method leveraging Rule-Based Rewards (RBRs) that aligns models to behave safely without extensive human data collection.

OpenAI and Los Alamos National Laboratory are working to develop safety evaluations to assess and measure biological capabilities and risks associated with frontier models.

Discover how prover-verifier games improve the legibility of language model outputs, making AI solutions clearer, easier to verify, and more trustworthy for both humans and machines.

Introducing the most cost-efficient small model in the market

Consistency models are a nascent family of generative models that can sample high quality data in one step without the need for adversarial training.

We present a holistic approach to building a robust and useful natural language classification system for real-world content moderation.