Inside our approach to the Model Spec

At OpenAI, we believe AI should be fair, safe, and freely available so that more people can use it to solve hard problems, create opportunities, and benefit in areas like health, science, education, work, and everyday life. We believe that democratized access to AI is the best path forward: not AI whose benefits or control are concentrated in the hands of a few, but AI that more people can access, understand, and help shape.

That is a core reason why the OpenAI Model Spec exists. The Model Spec⁠(opens in a new window) is our formal framework for model behavior. It defines how we want models to follow instructions, resolve conflicts, respect user freedom, and behave safely across the incredibly broad range of queries that users ask them daily. More broadly, it is our attempt to make intended model behavior explicit: not just inside our training process, but in a form that users, developers, researchers, policymakers, and the broader public can actually read, inspect, and debate.

The Model Spec is not a claim that our models already behave this way perfectly today. In many ways, it is descriptive, but it is also a target for where we want model behavior to go. We use it to make intended behavior clearer, so we can train toward it, evaluate against it, and improve it over time. 

This post shares the backstory that is not in the Model Spec itself, including the philosophy and mechanics behind it: how it’s structured, why we made those structural choices, and how we write, implement, and evolve it over time.

A public framework for model behavior

Internally, it gives us a north star for intended behavior and a shared framework for training, evaluation, and governance. Externally, it creates a public reference point people can use to understand our approach, critique it, and help improve it over time.

What’s in the Model Spec

High-level intent and public commitments

This preamble clarifies three goals for how we plan to pursue our mission: