Vals Legal Index
Agents are scored on Legal Research Bench, HLAB-Vals-1.1 and Tax Agent Bench, mapped to practice areas weighted by share of US law-office revenue.
Practice areas without a covering benchmark (7% of revenue) are not scored. 33 models on 2026-10-29, led by Wonder Full at 77.86%.
- Level
- Expert Specialists with years of training, often over hours of work
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Agent task Many steps in a terminal, browser, desktop, app, game or robot.
Muse Wonder Full leads with 77.9%
Best score per model from the benchmark's own leaderboard, read on 2 Nov 2026. Where a model ran with several settings or harnesses, we keep its best. Every run is in the next chart.
- 77.9
Muse Wonder Full
- 76.7
Gemini 4 Argon
- 75.9
Gemini 4 Carbon
- 75.3
Muse Wonder
- 74.6 GLM-5.3
- 73.9
Qwen3.8 Max (0902)
- 72.6 Grok 4.7
- 72.3 Claude Opus 5.5
- 72.1 MiMo-V2.6 Pro
- 71.9
DeepSeek V4.1 Flash 1017
- 71.7 Kimi K3
- 71.7
Tencent Hy4 preview
- 71.0 GLM-5.3-Flash
- 70.3 Claude Haiku 5.5
- 70.2
Step 5 Preview
- 69.8
Gemini 3.8 Flash
- 69.0
DeepSeek V4 Pro 0813
- 69.0 MiMo-V2.6 Flash
- 68.7 GPT-6.1 Sol
- 68.5
Muse Spark Open
- 68.4 GPT-6 Astra
- 68.0 Claude Sonnet 5.5
- 67.8
Mistral Large 4 (Le Chonk, open weights)
- 67.1
MiniMax-M3.1-Flash
- 67.0 Kimi K3.1 Pro
- 65.6
Mistral Large 4
- 63.0 Mercury 3
- 62.2 GPT-6 Luna
- 61.8 Beam 1.0
- 58.8 GPT-6.1 Terra
- 57.8
Inkling-Small
- 55.4
Nemotron 3.5 Super VL
- 54.2
Inkling
- 1
Muse Wonder Full 77.9%
- 2
Gemini 4 Argon 76.7%
- 3
Gemini 4 Carbon 75.9%
- 4
Muse Wonder 75.3%
- 5 GLM-5.3 74.6%
- 6
Qwen3.8 Max (0902) 73.9%
- 7 Grok 4.7 72.6%
- 8 Claude Opus 5.5 72.3%
- 9 MiMo-V2.6 Pro 72.1%
- 10
DeepSeek V4.1 Flash 1017 71.9%
- 11 Kimi K3 71.7%
- 12
Tencent Hy4 preview 71.7%
- 13 GLM-5.3-Flash 71%
- 14 Claude Haiku 5.5 70.3%
- 15
Step 5 Preview 70.2%
- 16
Gemini 3.8 Flash 69.8%
- 17
DeepSeek V4 Pro 0813 69%
- 18 MiMo-V2.6 Flash 69%
- 19 GPT-6.1 Sol 68.7%
- 20
Muse Spark Open 68.5%
- 21 GPT-6 Astra 68.4%
- 22 Claude Sonnet 5.5 68%
- 23
Mistral Large 4 (Le Chonk, open weights) 67.8%
- 24
MiniMax-M3.1-Flash 67.1%
- 25 Kimi K3.1 Pro 67%
- 26
Mistral Large 4 65.6%
- 27 Mercury 3 63%
- 28 GPT-6 Luna 62.2%
- 29 Beam 1.0 61.8%
- 30 GPT-6.1 Terra 58.8%
- 31
Inkling-Small 57.8%
- 32
Nemotron 3.5 Super VL 55.4%
- 33
Inkling 54.2%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 77.9% | Oct 2026 | Official leaderboard | |
| 2 | | 76.7% | Sep 2026 | Official leaderboard | |
| 3 | | 75.9% | Oct 2026 | Official leaderboard | |
| 4 | | 75.3% | Oct 2026 | Official leaderboard | |
| 5 | GLM-5.3 | 74.6% | Aug 2026 | Official leaderboard | |
| 6 | | 73.9% | Sep 2026 | Official leaderboard | |
| 7 | Grok 4.7 | 72.6% | Sep 2026 | Official leaderboard | |
| 8 | Claude Opus 5.5 | 72.3% | Sep 2026 | Official leaderboard | |
| 9 | MiMo-V2.6 Pro | 72.1% | Sep 2026 | Official leaderboard | |
| 10 | | 71.9% | Oct 2026 | Official leaderboard | |
| 11 | Kimi K3 | 71.7% | Jul 2026 | Official leaderboard | |
| 12 | | 71.7% | Aug 2026 | Official leaderboard | |
| 13 | GLM-5.3-Flash | 71% | Aug 2026 | Official leaderboard | |
| 14 | Claude Haiku 5.5 | 70.3% | Oct 2026 | Official leaderboard | |
| 15 | | 70.2% | Sep 2026 | Official leaderboard | |
| 16 | | 69.8% | Sep 2026 | Official leaderboard | |
| 17 | | 69% | Aug 2026 | Official leaderboard | |
| 18 | MiMo-V2.6 Flash | 69% | Sep 2026 | Official leaderboard | |
| 19 | GPT-6.1 Sol | 68.7% | Sep 2026 | Official leaderboard | |
| 20 | | 68.5% | Oct 2026 | Official leaderboard | |
| 21 | GPT-6 Astra | 68.4% | Sep 2026 | Official leaderboard | |
| 22 | Claude Sonnet 5.5 | 68% | Sep 2026 | Official leaderboard | |
| 23 | | 67.8% | Oct 2026 | Official leaderboard | |
| 24 | | 67.1% | Oct 2026 | Official leaderboard | |
| 25 | Kimi K3.1 Pro | 67% | Oct 2026 | Official leaderboard | |
| 26 | | 65.6% | Oct 2026 | Official leaderboard | |
| 27 | Mercury 3 | 63% | Oct 2026 | Official leaderboard | |
| 28 | GPT-6 Luna | 62.2% | Sep 2026 | Official leaderboard | |
| 29 | Beam 1.0 | 61.8% | Oct 2026 | Official leaderboard | |
| 30 | GPT-6.1 Terra | 58.8% | Oct 2026 | Official leaderboard | |
| 31 | | 57.8% | Jul 2026 | Official leaderboard | |
| 32 | | 55.4% | Oct 2026 | Official leaderboard | |
| 33 | | 54.2% | Jul 2026 | Official leaderboard |
The best score, 77.9%, costs $3.72 a task
Runs on the cost frontier score higher than any run that's cheaper. The costs are the leaderboard's own estimates, so compare them only within this benchmark.
| Model | Effort | Harness | Score | Cost per task | Output tokens |
|---|---|---|---|---|---|
| | 77.9% | $3.72 | |||
| | 76.7% | $6.82 | |||
| | 75.9% | $7.20 | |||
| | 75.3% | $4.32 | |||
| GLM-5.3 | 74.6% | $2.68 | |||
| | 73.9% | $4.25 | |||
| Grok 4.7 | 72.6% | $5.82 | |||
| Claude Opus 5.5 | 72.3% | $21 | |||
| MiMo-V2.6 Pro | 72.1% | $0.18 | |||
| | 71.9% | $0.59 | |||
| Kimi K3 | 71.7% | $3.47 | |||
| | 71.7% | $0.81 | |||
| GLM-5.3-Flash | 71% | $0.19 | |||
| Claude Haiku 5.5 | 70.3% | $2.36 | |||
| | 70.2% | $0.84 | |||
| | 69.8% | $2.05 | |||
| | 69% | $0.87 | |||
| MiMo-V2.6 Flash | 69% | $0.07 | |||
| GPT-6.1 Sol | 68.7% | $2.75 | |||
| | 68.5% | $5.36 | |||
| GPT-6 Astra | 68.4% | $14 | |||
| Claude Sonnet 5.5 | 68% | $14 | |||
| | 67.8% | $0.78 | |||
| | 67.1% | $0.41 | |||
| Kimi K3.1 Pro | 67% | $6.63 | |||
| | 65.6% | $2.22 | |||
| Mercury 3 | 63% | $0.10 | |||
| GPT-6 Luna | 62.2% | $0.31 | |||
| Beam 1.0 | 61.8% | ||||
| GPT-6.1 Terra | 58.8% | $2.29 | |||
| | 57.8% | $0.14 | |||
| | 55.4% | $0.16 | |||
| | 54.2% | $0.70 |
2 models have held number one since its release in Oct 2026
Lab claims count from the day the card came out, independent runs from the model’s release.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No lab model card reports it yet
We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark.
More in Economic work
- JobBench In use 130 tasks from 35 occupations workers most want delegated. Agent works through messy reference files, graded by fact-anchored binary rubrics. GLM-5.3 61.4%
- GDPval In use Real work tasks from 44 occupations with reference files. Experts blindly compare model deliverables with expert work. Metric is win-or-tie rate.
Seed2.1 Pro 87.9%
- GDPval-AA v2.1 In use Agents with shell and web access complete 220 GDPval gold tasks from 44 occupations. Blind pairwise comparisons give Elo ratings. Claude Opus 5.5 1,846
- AA-Briefcase v1.1 In use Version 1.1 of Artificial Analysis's multi-hour office work evaluation, with Elo ratings fitted by Crowd-BT. Claude Opus 5.5 1,822
- CoWorkBench In use Agent completes long-horizon productivity tasks across computer science, finance, law and medicine. Qwen in-house scoring (method unpublished).
Qwen3.8-Omni-Flash 75.3%
- OfficeQA In use Grounded reasoning over a large corpus of U.S. Treasury Bulletin documents: locate tables and compute precise numeric answers. Claude Opus 4.7 86.3%