Some 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 Open AI'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.

Astra’s standard API rates are $10 per million input tokens and $50 per million output tokens. At fixed usage of 100,000 input and 10,000 output tokens, Astra costs 2.5 times Sol, about 4.7 times Terra, and about 47 times Luna. These are meaningful premiums for reasoning-intensive tasks.

Stronger reasoning comes at a premium, and teams must evaluate where the extra capability justifies the cost. Astra’s advantage is most pronounced in complex, distributed reasoning scenarios, not routine fixes.