legal benchmark
The Legal Agent Benchmark (LAB) is Harvey's open-source benchmark for evaluating AI agents on complex, long-horizon legal work. Tasks are scored under an all-pass standard against expert-curated rubrics, where a task passes only if every required rubric criterion (facts, conclusions, citations, structure, and analytical moves) passes.
Updated Aug 11, 2026
Higher score ranks better on this benchmark.
| 01 | AN | 13.3% | 100.0% | 13 | C | |
| 02 | AN | 11.7% | 91.7% | 13 | C | |
| 03 | AN | 7.1% | 83.3% | 13 | B | |
| 04 | AN | 5.8% | 75.0% | 13 | C | |
| 05 | AN | 5.4% | 66.7% | 13 | B | |
| 06 | AN | 4.2% | 58.3% | 13 | B | |
| 07 | OP | 2.1% | 50.0% | 13 | B | |
| 08 | GO | 0.8% | 41.7% | 13 | B | |
| 09 | OP | 0.4% | 33.3% | 13 | B | |
| 10 | GO | 0.0% | 25.0% | 13 | B | |
| 11 | GO | 0.0% | 16.7% | 13 | B | |
| 12 | GO | 0.0% | 8.3% | 13 | B | |
| 13 | OP | 0.0% | 0.0% | 13 | B |
A closer view of the leading scores on this benchmark.
The leading models and scores on this benchmark.
What Legal Agent Benchmark measures and how its scores work.
The Legal Agent Benchmark (LAB) is Harvey's open-source benchmark for evaluating AI agents on complex, long-horizon legal work. Tasks are scored under an all-pass standard against expert-curated rubrics, where a task passes only if every required rubric criterion (facts, conclusions, citations, structure, and analytical moves) passes.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about Legal Agent Benchmark.
Claude Fable 5 is currently ranked first with 13.3%.
The Legal Agent Benchmark (LAB) is Harvey's open-source benchmark for evaluating AI agents on complex, long-horizon legal work. Tasks are scored under an all-pass standard against expert-curated rubrics, where a task passes only if every required rubric criterion (facts, conclusions, citations, structure, and analytical moves) passes.
Yes. Higher values rank better for this benchmark.
13 model results are currently shown.
Yes. This benchmark can contribute to the current LLMBoard capability score.