GoKawiilSome of the hardest work in code review happens outside the changed lines. A change can look correct in isolation and still break code elsewhere in the system.
That is what makes our early results for OpenAI's GPT-6 Astra most interesting. In our evaluation, Astra caught approximately 4% more labeled bugs through actionable findings than GPT-5.6 Sol, and 22% more than Opus 5.
The biggest jump comes on harder cross-file reviews, where Astra's gains reach 20% over Sol and 33% over Opus 5. Using that capability at customer scale also means protecting customer data and assessing the model’s public API pricing.
What Astra added to code review
Our measure here is actionable bug coverage, meaning how many labeled bugs a model catches through findings a developer can act on.
Coverage is rounded to one decimal; relative gains use unrounded values.
The overall gain over GPT-5.6 Sol appears modest in this first evaluation measure. This evaluation also includes simpler reviews, where there may be less room for a stronger model to differentiate itself; Astra's larger advantage appears in the harder cross-file subset. It is an early, directional result.
Coverage is rounded to one decimal; relative gains use unrounded values. Compare models within this chart, since its review difficulty differs from the overall evaluation.
The harder cross-file comparison is more encouraging. Astra's relative advantage grows to 20% over Sol and 33% over Opus 5. That suggests value in connecting a change's intent to consequences distributed across a codebase.
These results describe one part of review performance. They do not establish an overall ranking of review quality, predict a team's defect rate, or promise the same gain on every pull request.
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