Predicting model behavior before release by simulating deployment

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 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, Deployment Simulation improved our estimates of undesired model behavior rates, helped surface novel forms of misalignment before release, and helped reduce the risk that models would be able to tell they were being tested. We also applied the method to challenging agentic rollouts, showing that it can extend beyond standard chat to more complex agent settings involving tool use, and can also be used for risk assessment before internal model deployments.

We have already used insights from Deployment Simulation during model development to identify blind spots in traditional evaluations and inform mitigations and deployment decisions. As we make the pipeline easier to run, we expect it to play a larger role in the future model development process.

How Deployment Simulation works

High level comparison between traditional evaluations and Deployment Simulation