GPT‐6 Sol and GPT‐6 Luna: October 2026 update

We conducted benchmark evaluations across safety categories. We report here on our Production Benchmarks, an evaluation set with conversations representative of challenging examples from production data. As we noted in previous system cards, we introduced these Production Benchmarks to help us measure continuing progress given that earlier Standard evaluations for these categories had become relatively saturated.

These evaluations were deliberately created to be difficult. They were built around cases in which our existing models were not yet giving ideal responses, and this is reflected in the scores below. Error rates are not representative of average production traffic. The primary metric is safe completion rate, checking that the model's response and actions are not disallowed according to our safety policies. Our evaluations are run on the model without system-level safeguards to ensure the model's underlying behavior meets our safety bar. We continue monitoring these categories after launch to evaluate online performance and further adjust safeguards as appropriate.

Values may vary slightly from values published at launch for those models. Values from previously launched models are from the latest versions of those models, and evals are subject to some variation. The comparison scores from earlier models listed below are intended to shed light on relative performance. Because policies, graders, datasets, evaluations, and other measurement details evolve over time, scores from previous system cards not included in the table below should generally not be considered directly comparable to these most recent results.

Relative to their respective GPT-5.6 counterparts, GPT-6 Sol (October) shows a statistically significant regression on standard self-harm, while GPT-6 Luna (October) shows statistically significant regressions on standard self-harm, gore, and sexual content.

We manually reviewed violative responses from these evaluations and found that violations were borderline but still generally safe. For example, GPT-6 Sol (October) and GPT-6 Luna (October) are more willing to answer informational questions about self-harm while directing users to appropriate professional resources. Based on our evaluations, it does not comply with requests to facilitate self-harm. GPT-6 Sol (October) and GPT-6 Luna (October) are more likely to engage with requests for gore and sexual content rather than refuse outright, however the outputs we reviewed were lower severity. We also conducted adversarial red teaming and did not identify any high-severity risks based on our evaluation criteria.

These evaluations do not account for system-level interventions that elevate the holistic safety of the assistant response, such as Trusted Contact, localized crisis helplines, and Parental Controls for younger users.