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)
| Model | Score |
|---|---|
| GPT-6 Astra | 91.7 |
| GPT-6.1 Sol | 91.4 |
| GPT-6.1 Terra | 91.2 |
| GPT-6 Luna | 89.1 |
| GPT-6 Sol | 88.0 |
| GPT-5.6 Sol | 86.2 |
| Claude Opus 5.5 | 84.0 |
| Grok 4.6 | 83.3 |
| Kimi K3 | 80.3 |
| Gemini 3.8 Flash | 78.9 |
| GLM-5.3 | 78.4 |
| DeepSeek V4.1 Flash | 65.0 |
| Claude Opus 5 | 64.2 |
| Muse Wonder | 63.6 |