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Predicting model behavior before release by simulating deployment | OpenAI

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Before releasing a new model, labs need to understand not just what it can do, but how it is likely to behave in real-world use, including where it might introduce new risks. This becomes even more important as capabilities increase. As part of our pre-deployment safety review, we leverage targeted evaluations, red-teaming, and other checks to understand model behavior. We’ve now started using a method for simulating model deployments before they happen, which adds a complementary signal: a deployment-like preview of how a candidate model may behave before it reaches users. Deployment Simulation is a method for simulating a future deployment before it happens. We do so by replaying previous conversations in a privacy-preserving manner with a new candidate model. This enables us to study how the new model responds in realistic contexts before release, including whether new undesired behaviors emerge and how often they may appear. Across multiple GPT‑5‑series Thinking deployments, Deploy

June 16, 2026 Research Predicting model behavior before release by simulating deployment Using realistic conversation contexts to better estimate undesired model behavior before release. Read the paper Share Introduction Before releasing a new model, labs need to understand not just what it can do, but how it is likely to behave in real-world use, including where it might introduce new risks. This becomes even more important as capabilities increase. As part of our pre-deployment safety review, we leverage targeted evaluations, red-teaming, and other checks to understand model behavior. We’ve

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