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.

Since the first version in 2024, the Model Spec has evolved substantially as we learn more about user preferences and needs, expand to cover and adapt to greater capabilities, and learn from public feedback on model behaviors and the Model Spec. In the spirit of iterative deployment ⁠ , the Model Spec is an evolving document covering both background values and explicit, legible rules—paired with a process for modifying individual elements as we learn from real-world deployment and feedback. We are also investing in public feedback mechanisms like collective alignment ⁠ to help keep humanity in control of how AI is used and how AI behavior is shaped.

At the core of the Model Spec is the Chain of Command: a framework for deciding which instructions should apply in a given situation. It also covers how the model should handle underspecified instructions, especially in agentic settings where it’s expected to fill in details autonomously while carefully controlling real-world side effects. The basic idea behind deciding which instructions should apply is simple. Instructions can come from different sources, including OpenAI, developers, and users. Those instructions can conflict. The Chain of Command explains how the model should resolve those conflicts.

Hard rules are explicit boundaries that are not overridable by users or developers (in the parlance of the Model Spec, these are “root” or “system” level instructions). They are mostly prohibitive, requiring models to avoid behaviors that could contribute to catastrophic risks or direct physical harm, violate laws, or undermine the chain of command. We expect AI to become a foundational technology for society, analogous to basic internet infrastructure, so we only impose rules that could limit intellectual freedom when we believe they are necessary for the broad spectrum of developers and users who will interact with it. In the Model Spec, Stay in bounds ⁠ (opens in a new window) contains hard rules that address concrete real-world safety risks, and Under-18 Principles ⁠ (opens in a new window) layers on additional safeguards for users under 18.

It then explains how we think about balancing these goals in practice, making the tradeoffs concrete enough to support the more detailed principles that follow.

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.

Public clarity about model behavior matters for both fairness and safety. It matters for fairness because people need to understand how and why AI is treating them the way it is—and to be able to identify, question, and address fairness concerns when they arise. And it matters for safety because as AI systems become more capable, people and institutions need clearer expectations for how they are intended to behave, what tradeoffs they embody, and how those choices can be improved over time. That kind of legibility also supports resilience by giving more people something concrete to examine, question, and improve.