MacroscopeBench
Models review commits from public repositories and must flag the known bugs a commit introduced without false reports. LLM judges match findings to the bugs.
195 tasks include control commits with no known bugs. Scored by combined precision and recall. 41 models in the Fireworks index.
- Made by
- Macroscope
- Kind
- Fixing real repositories
- Level
- Expert
- Scored
- Graded by a model
- Answers
- Open-ended
- Tasks
- Kept private
- Strongest model tested
- Claude Fable 5.2 #1 on the capability index
- Last model added
- November 2026
- Scores refreshed
- November 12th, 2026
Claude Opus 5.5 leads with 83.9%
Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart.
- 1 Claude Opus 5.5 max effort 83.9%
- 2 GPT-6.1 Terra high effort 82.3%
- 3 GPT-6 Sol max effort 80.3%
- 4 GPT-6.1 Sol max effort 78.5%
- 5 Claude Fable 5.2 xhigh effort 78.5%
- 6 GPT-6 Astra max effort 78%
- 7 GPT-5.6 Sol high effort 77.8%
- 8 GLM-5.3 max effort 77.4%
- 9 Grok 4.6 xhigh effort 75.9%
- 10 Kimi K3 max effort 72.9%
- 11
Gemini 4 Argon high effort 72.8%
- 12 GPT-6 Luna max effort 72.5%
- 13
DeepSeek V4.1 Flash max effort 72%
- 14
Gemini 3.8 Flash medium effort 70.7%
- 15 Claude Opus 5 high effort 69.9%
- 16
Muse Wonder low effort 64.2%
Source: Official MacroscopeBench leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5.5 | 83.9% | max effort | Sep 2026 | Official leaderboard |
| 2 | GPT-6.1 Terra | 82.3% | high effort | Oct 2026 | Official leaderboard |
| 3 | GPT-6 Sol | 80.3% | max effort | Sep 2026 | Official leaderboard |
| 4 | GPT-6.1 Sol | 78.5% | max effort | Sep 2026 | Official leaderboard |
| 5 | Claude Fable 5.2 | 78.5% | xhigh effort | Nov 2026 | Official leaderboard |
| 6 | GPT-6 Astra | 78% | max effort | Sep 2026 | Official leaderboard |
| 7 | GPT-5.6 Sol | 77.8% | high effort | Jul 2026 | Official leaderboard |
| 8 | GLM-5.3 | 77.4% | max effort | Aug 2026 | Official leaderboard |
| 9 | Grok 4.6 | 75.9% | xhigh effort | Aug 2026 | Official leaderboard |
| 10 | Kimi K3 | 72.9% | max effort | Jul 2026 | Official leaderboard |
| 11 | | 72.8% | high effort | Sep 2026 | Official leaderboard |
| 12 | GPT-6 Luna | 72.5% | max effort | Sep 2026 | Official leaderboard |
| 13 | | 72% | max effort | Sep 2026 | Official leaderboard |
| 14 | | 70.7% | medium effort | Sep 2026 | Official leaderboard |
| 15 | Claude Opus 5 | 69.9% | high effort | Jul 2026 | Official leaderboard |
| 16 | | 64.2% | low effort | Oct 2026 | Official leaderboard |
Score vs. inference cost
The line joins the runs that no other run beats on both measures. We redrew this chart from the numbers on the official leaderboard.
| Model | Setting | Score | Cost per code review |
|---|---|---|---|
| Claude Opus 5.5 | max effort | 83.9 | $11.58 |
| GPT-6.1 Terra | high effort | 82.3 | $0.57 |
| GPT-6.1 Terra | medium effort | 81.2 | $0.35 |
| GPT-6 Sol | max effort | 80.3 | $3.68 |
| Claude Opus 5.5 | xhigh effort | 78.9 | $3.51 |
| GPT-6.1 Sol | max effort | 78.5 | $1.05 |
| Claude Fable 5.2 | xhigh effort | 78.5 | $7.41 |
| GPT-6 Astra | max effort | 78 | $7.87 |
| GPT-5.6 Sol | high effort | 77.8 | $3.9 |
| GLM-5.3 | max effort | 77.4 | $3.86 |
| GPT-6.1 Sol | xhigh effort | 77.2 | $0.79 |
| GPT-6 Sol | xhigh effort | 77.2 | $2 |
| GPT-6.1 Sol | high effort | 76.7 | $0.56 |
| GPT-6.1 Terra | low effort | 76.1 | $0.18 |
| Claude Fable 5.2 | high effort | 76 | $3.01 |
| Grok 4.6 | xhigh effort | 75.9 | $4.68 |
| Grok 4.6 | high effort | 75.1 | $3.51 |
| GPT-6 Sol | high effort | 74.3 | $1.23 |
| GPT-6.1 Sol | medium effort | 73.5 | $0.28 |
| Claude Opus 5.5 | high effort | 73.1 | $1.3 |
| Kimi K3 | max effort | 72.9 | $2.82 |
| | high effort | 72.8 | $1.71 |
| Grok 4.6 | medium effort | 72.8 | $2.56 |
| Claude Fable 5.2 | medium effort | 72.7 | $1.22 |
| GPT-6 Luna | max effort | 72.5 | $0.13 |
| | max effort | 72 | $0.75 |
| Claude Fable 5.2 | low effort | 71.1 | $1 |
| | low effort | 70.7 | $0.38 |
| | medium effort | 70.7 | $3.38 |
| GPT-6.1 Sol | low effort | 70.6 | $0.16 |
| | high effort | 70.5 | $0.38 |
| | medium effort | 70.3 | $0.51 |
| Claude Opus 5.5 | medium effort | 70.1 | $0.97 |
| Claude Opus 5 | high effort | 69.9 | $6.5 |
| GLM-5.3 | high effort | 69.4 | $2.02 |
| GPT-6 Sol | medium effort | 69.2 | $0.69 |
| Kimi K3 | high effort | 68.6 | $1.9 |
| GPT-6 Luna | xhigh effort | 68.2 | $0.06 |
| GPT-5.6 Sol | low effort | 66.1 | $0.81 |
| GPT-6 Luna | high effort | 65.6 | $0.05 |
| Claude Opus 5 | medium effort | 65.5 | $3.5 |
| | low effort | 64.2 | $3.3 |
| GPT-6 Luna | medium effort | 62.4 | $0.03 |
| GPT-6 Sol | low effort | 61.8 | $0.36 |
| Claude Opus 5.5 | low effort | 61.4 | $0.68 |
| | low effort | 61.3 | $0.43 |
| Claude Opus 5 | low effort | 59.4 | $1.4 |
| Grok 4.6 | low effort | 57.1 | $0.3 |
| GLM-5.3 | low effort | 54.6 | $0.41 |
| Kimi K3 | low effort | 53.5 | $0.48 |
| | low effort | 51.3 | $0.9 |
| GPT-6 Luna | low effort | 44.7 | $0.02 |
The best score, 83.9%, costs $12 a task
Runs on the cost frontier score higher than any run that's cheaper. GPT-6.1 Terra at medium effort comes within 2.7 points of the best for $0.35 a task, 33 times cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
Source: Official MacroscopeBench leaderboard
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| Claude Opus 5.5 | max effort | 83.9% | $12 | ||
| GPT-6.1 Terra | high effort | 82.3% | $0.57 | ||
| GPT-6.1 Terra | medium effort | 81.2% | $0.35 | ||
| GPT-6 Sol | max effort | 80.3% | $3.68 | ||
| Claude Opus 5.5 | xhigh effort | 78.9% | $3.51 | ||
| GPT-6.1 Sol | max effort | 78.5% | $1.05 | ||
| Claude Fable 5.2 | xhigh effort | 78.5% | $7.41 | ||
| GPT-6 Astra | max effort | 78% | $7.87 | ||
| GPT-5.6 Sol | high effort | 77.8% | $3.90 | ||
| GLM-5.3 | max effort | 77.4% | $3.86 | ||
| GPT-6 Sol | xhigh effort | 77.2% | $2.00 | ||
| GPT-6.1 Sol | xhigh effort | 77.2% | $0.79 | ||
| GPT-6.1 Sol | high effort | 76.7% | $0.56 | ||
| GPT-6.1 Terra | low effort | 76.1% | $0.18 | ||
| Claude Fable 5.2 | high effort | 76% | $3.01 | ||
| Grok 4.6 | xhigh effort | 75.9% | $4.68 | ||
| Grok 4.6 | high effort | 75.1% | $3.51 | ||
| GPT-6 Sol | high effort | 74.3% | $1.23 | ||
| GPT-6.1 Sol | medium effort | 73.5% | $0.28 | ||
| Claude Opus 5.5 | high effort | 73.1% | $1.30 | ||
| Kimi K3 | max effort | 72.9% | $2.82 | ||
| Grok 4.6 | medium effort | 72.8% | $2.56 | ||
| | high effort | 72.8% | $1.71 | ||
| Claude Fable 5.2 | medium effort | 72.7% | $1.22 | ||
| GPT-6 Luna | max effort | 72.5% | $0.13 | ||
| | max effort | 72% | $0.75 | ||
| Claude Fable 5.2 | low effort | 71.1% | $1.00 | ||
| | medium effort | 70.7% | $3.38 | ||
| | low effort | 70.7% | $0.38 | ||
| GPT-6.1 Sol | low effort | 70.6% | $0.16 | ||
| | high effort | 70.5% | $0.38 | ||
| | medium effort | 70.3% | $0.51 | ||
| Claude Opus 5.5 | medium effort | 70.1% | $0.97 | ||
| Claude Opus 5 | high effort | 69.9% | $6.50 | ||
| GLM-5.3 | high effort | 69.4% | $2.02 | ||
| GPT-6 Sol | medium effort | 69.2% | $0.69 | ||
| Kimi K3 | high effort | 68.6% | $1.90 | ||
| GPT-6 Luna | xhigh effort | 68.2% | $0.06 | ||
| GPT-5.6 Sol | low effort | 66.1% | $0.81 | ||
| GPT-6 Luna | high effort | 65.6% | $0.05 | ||
| Claude Opus 5 | medium effort | 65.5% | $3.50 | ||
| | low effort | 64.2% | $3.30 | ||
| GPT-6 Luna | medium effort | 62.4% | $0.03 | ||
| GPT-6 Sol | low effort | 61.8% | $0.36 | ||
| Claude Opus 5.5 | low effort | 61.4% | $0.68 | ||
| | low effort | 61.3% | $0.43 | ||
| Claude Opus 5 | low effort | 59.4% | $1.40 | ||
| Grok 4.6 | low effort | 57.1% | $0.30 | ||
| GLM-5.3 | low effort | 54.6% | $0.41 | ||
| Kimi K3 | low effort | 53.5% | $0.48 | ||
| | low effort | 51.3% | $0.90 | ||
| GPT-6 Luna | low effort | 44.7% | $0.02 |
3 models have held number one on the official leaderboard since 2023
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official MacroscopeBench leaderboard
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Official MacroscopeBench leaderboard
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
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