Introducing gpt-oss-safeguard

Today, we’re releasing a research preview of gpt-oss-safeguard, our open-weight reasoning models for safety classification tasks, available in two sizes: gpt-oss-safeguard-120b and gpt-oss-safeguard-20b. These models are fine-tuned versions of our gpt-oss⁠ open models and available under the same permissive Apache 2.0 license, allowing anyone to use, modify, and deploy them freely. Both models can be downloaded today from Hugging Face⁠(opens in a new window).

The gpt-oss-safeguard models use reasoning to directly interpret a developer-provided policy at inference time—classifying user messages, completions, and full chats according to the developer’s needs. The developer always decides what policy to use, so responses are more relevant and tailored to the developer’s use case. The model uses chain-of-thought, which the developer can review to understand how the model is reaching its decisions. Additionally, the policy is provided during inference, rather than being trained into the model, so it is easy for developers to iteratively revise policies to increase performance. This approach, which we initially developed for internal use, is significantly more flexible than the traditional method of training a classifier to indirectly infer a decision boundary from a large number of labeled examples.

gpt-oss-safeguard enables developers to draw the policy lines that best fit their use case. For instance, a video gaming discussion forum might want to develop a policy to classify posts that discuss cheating in the game, or a product reviews site might want to use its own policy to screen reviews that appear likely to be fake.

The model takes two inputs at once—a policy and the content to classify under that policy—and outputs a conclusion about where the content falls, along with its reasoning. Developers decide how, if at all, to use those conclusions in their own safety pipelines. We’ve seen this reasoning-based approach perform especially well in situations where: