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MacroscopeBench Precision

Code · 2026-09-22

MacroscopeBench precision: the share of all findings a reviewer reports across the benchmark that a second LLM judge, which can explore the codebase, rates as valid correctness bugs introduced or exposed by the reviewed commit; style, subjective and out-of-scope findings count against it. It is averaged over three runs, in percent. The denominator is each model's own finding count, so it is a diagnostic rather than a fixed-item score.

Top models (higher is better)

ModelScore
GPT-6 Astra91.7
GPT-6.1 Sol91.4
GPT-6.1 Terra91.2
GPT-6 Luna89.1
GPT-6 Sol88.0
GPT-5.6 Sol86.2
Claude Opus 5.584.0
Grok 4.683.3
Kimi K380.3
Gemini 3.8 Flash78.9
GLM-5.378.4
DeepSeek V4.1 Flash65.0
Claude Opus 564.2
Muse Wonder63.6
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