GPT-6 Astra in code review: Gains, privacy, and cost

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 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. Putting context to work A large context window creates room for information. Useful reasoning requires the model to identify which pieces matter, connect them, and reach a conclusion supported by evidence. Our interpretation is that Astra's most interesting advance lies in connecting the right information. Its larger gains on harder cross-file reviews suggest progress on work where relevant information is distributed. They do not isolate the cause of that progress or prove that more context alone improves a model's performance. OpenAI positions Astra for multistep work across code, browsers, and professional software. For teams building with models, the useful question is where the extra reasoning changes the outcome enough to justify its cost. A difficult task with scattered evidence is a stronger candidate to investigate than a routine task that a less expensive model already handles reliably. That doesn't mean switching every session to Astra, maxing out reasoning effort, and letting it rip. Stronger reasoning comes at a premium Astra’s standard API rates are $10 per million input tokens and $50 per million output tokens, according to Astra’s published pricing. Fable 5.1 has the same base input and output rates, although caching prices differ. Anthropic’s pricing documentation provides the full breakdown. To put those prices in context, consider an illustrative task using 100,000 uncached input tokens and 10,000 billable output tokens, including reasoning tokens. Holding token usage constant makes the published rates easier to compare; actual task costs vary with usage. All figures use publicly listed standard API prices, checked September 4, 2026. OpenAI figures use short-context rates. Sol's public promotional pricing is available at least through November 21, 2026. The example excludes caching, cache writes, tools, retries, regional uplifts, and service-tier adjustments. Real tasks can use different numbers of tokens. ModelInput / 1M tokensOutput / 1M tokensIllustrative task cost GPT-5.6 Luna$0.20$1.20$0.032 GPT-5.6 Terra$2.00$12.00$0.32 GPT-5.6 Sol$4.00$20.00$0.60 GPT-6 Astra$10.00$50.00$1.50 Claude Fable 5.1$10.00$50.00$1.50 At that fixed usage, Astra costs 2.5 times Sol, about 4.7 times Terra, and about 47 times Luna. Those are meaningful premiums. They are not predictions of the difference in cost per completed task. A model that needs fewer tokens or fewer attempts could narrow the gap. OpenAI reports lower estimated task costs for Astra in some of its own evaluations despite higher token prices. That makes total cost per successful outcome worth measuring on your work, rather than assuming that either token price or a capability score settles the decision. Read OpenAI's efficiency guidance. What might transfer beyond code review The transferable idea is reasoning over relationships among separate sources. Our findings suggest several uses worth evaluating; we have not measured Astra on these tasks: Research synthesis: reconcile conflicting reports, connect claims to their evidence, and identify gaps that a summary of each document would miss. Operational investigation: assemble a coherent explanation from logs, incident notes, and runbooks, while separating observations from hypotheses. Requirements and policy analysis: trace a proposed change across specifications, internal policies, and implementation plans to flag inconsistencies for expert review. Document and spreadsheet work: check whether assumptions, formulas, and narrative conclusions agree across a report and its supporting materials. The common structure is scattered evidence with dependencies between its parts. Start with bounded work whose answer can be checked. The easiest way to test whether Astra is right for your workflows or products is to run it alongside your current model on the same tasks, then compare answer quality, verification time, and total cost.