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.

The Model Spec is one part of OpenAI’s broader approach to safe and accountable AI. While the Preparedness Framework⁠ focuses on risks from frontier capabilities and the safeguards required as those risks rise, the Model Spec addresses a different but complementary question: how our models should behave across a wide range of situations. Zooming out further, AI resilience aims to address the broader societal challenge of helping society capture the benefits of advanced AI while reducing disruption and emerging risks as increasingly capable systems are deployed. Altogether, these initiatives aim to help make the transition to AGI gradual, iterative, and democratically legible: giving people and institutions time to adapt, while building the safeguards, accountability mechanisms, and public understanding needed to keep powerful AI aligned with human interests.