PRBench
Model answers 1,100 expert-written law and finance questions. LLM judge grades against weighted expert rubrics of 10-30 criteria each.
Hard subset of 550 tasks. Top Hard scores about 0.39 at release.
In the authors’ words
Frontier model progress is often measured by academic benchmarks, which offer a limited view of performance in real-world professional contexts. Existing evaluations often fail to assess open-ended, economically consequential tasks in high-stakes domains like Legal and Finance, where practical returns are paramount. To address this, we introduce Professional Reasoning Bench (PRBench), a realistic, open-ended, and difficult benchmark of real-world problems in Finance and Law. We open-source its 1,100 expert-authored tasks and 19,356 expert-curated criteria, making it, to our knowledge, the largest public, rubric-based benchmark for both legal and finance domains. We recruit 182 qualified professionals, holding JDs, CFAs, or 6+ years of experience, who contributed tasks inspired by their actual workflows. This process yields significant diversity, with tasks spanning 114 countries and 47 US jurisdictions. Our expert-curated rubrics are validated through a rigorous quality pipeline, including independent expert validation. Subsequent evaluation of 20 leading models reveals substantial room for improvement, with top scores of only 0.39 (Finance) and 0.37 (Legal) on our Hard subsets. We further catalog associated economic impacts of the prompts and analyze performance using human-annotated rubric categories. Our analysis shows that models with similar overall scores can diverge significantly on specific capabilities. Common failure modes include inaccurate judgments, a lack of process transparency and incomplete reasoning, highlighting critical gaps in their reliability for professional adoption.
- 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
- Open-ended Free-form text, documents, designs or media judged as a whole.
Muse Spark 1.3 leads with 61.6%
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.
- 61.6
Muse Spark 1.3
- 59.8
Muse Spark Open
- 58.4
Muse Wonder
- 57.0
Muse Spark 1.1
- 52.6 Claude Fable 5
- 52.3
Muse Spark
- 52.3 Claude Opus 4.6
- 51.6 Claude Fable 5.1
- 50.5 GPT-5.6 Sol
- 49.9 GPT-5 Pro
- 49.7 o3-pro
- 49.4
Gemini 4 Argon
- 49.3 GPT-5.1
- 49.0 GPT-5
- 48.9
Gemini 3.8 Flash
- 48.6 o3
- 48.4 GPT-6 Astra
- 47.9 GPT-6.1 Sol
- 47.6 Grok 4.7
- 47.6 Claude Opus 5.5
- 47.3
DeepSeek V4.1 Flash 1017
- 45.4 GPT-5.2 Pro
- 44.4 GPT-5.4
- 44.2 Claude Opus 4.5
- 44.0
Gemini 3.1 Pro
- 43.8 Kimi K2.5
- 42.9 GPT-6 Luna
- 41.4
Gemini 2.5 Pro
- 41.0
Gemini 2.5 Flash
- 40.9 Kimi K2 Thinking
- 40.8 Claude Sonnet 4.5
- 40.6
Gemini 3 Pro
- 40.2 gpt-oss-120b
- 39.5
Mistral Medium
- 38.6 GPT-6 Sol
- 38.3
Qwen3-235B-A22B-Instruct
- 38.1 o4-mini
- 37.6
DeepSeek-V3.1
- 36.6
DeepSeek-R1
- 36.5 GPT-4.1
- 36.4 Kimi K2
- 34.0 Claude Opus 4.1
- 30.4 GPT-4.1 mini
- 24.8
Llama 4 Maverick
- 1
Muse Spark 1.3 61.6%
- 2
Muse Spark Open 59.8%
- 3
Muse Wonder 58.4%
- 4
Muse Spark 1.1 57%
- 5 Claude Fable 5 52.6%
- 6
Muse Spark 52.3%
- 7 Claude Opus 4.6 non-thinking 52.3%
- 8 Claude Fable 5.1 51.6%
- 9 GPT-5.6 Sol max 50.5%
- 10 GPT-5 Pro 49.9%
- 11 o3-pro 49.7%
- 12
Gemini 4 Argon 49.4%
- 13 GPT-5.1 thinking 49.3%
- 14 GPT-5 49%
- 15
Gemini 3.8 Flash 48.9%
- 16 o3 48.6%
- 17 GPT-6 Astra 48.4%
- 18 GPT-6.1 Sol 47.9%
- 19 Grok 4.7 xhigh 47.6%
- 20 Claude Opus 5.5 47.6%
- 21
DeepSeek V4.1 Flash 1017 47.3%
- 22 GPT-5.2 Pro 45.4%
- 23 GPT-5.4 high 44.4%
- 24 Claude Opus 4.5 thinking 44.2%
- 25
Gemini 3.1 Pro 44%
- 26 Kimi K2.5 43.8%
- 27 GPT-6 Luna 42.9%
- 28
Gemini 2.5 Pro Jun 2025 41.4%
- 29
Gemini 2.5 Flash Jun 2025 41%
- 30 Kimi K2 Thinking 40.9%
- 31 Claude Sonnet 4.5 40.8%
- 32
Gemini 3 Pro 40.6%
- 33 gpt-oss-120b 40.2%
- 34
Mistral Medium 39.5%
- 35 GPT-6 Sol 38.6%
- 36
Qwen3-235B-A22B-Instruct Jul 2025 38.3%
- 37 o4-mini 38.1%
- 38
DeepSeek-V3.1 37.6%
- 39
DeepSeek-R1 May 2025 36.6%
- 40 GPT-4.1 36.5%
- 41 Kimi K2 Jul 2025 36.4%
- 42 Claude Opus 4.1 34%
- 43 GPT-4.1 mini 30.4%
- 44
Llama 4 Maverick 24.8%
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 61.6% | Sep 2026 | Official leaderboard | |
| 2 | | 59.8% | Oct 2026 | Official leaderboard | |
| 3 | | 58.4% | Oct 2026 | Official leaderboard | |
| 4 | | 57% | Jul 2026 | Official leaderboard | |
| 5 | Claude Fable 5 | 52.6% | Jun 2026 | Official leaderboard | |
| 6 | | 52.3% | Apr 2026 | Official leaderboard | |
| 7 | Claude Opus 4.6 | 52.3% | non-thinking | Feb 2026 | Official leaderboard |
| 8 | Claude Fable 5.1 | 51.6% | Sep 2026 | Official leaderboard | |
| 9 | GPT-5.6 Sol | 50.5% | max | Jul 2026 | Official leaderboard |
| 10 | GPT-5 Pro | 49.9% | Oct 2025 | Official leaderboard | |
| 11 | o3-pro | 49.7% | Jun 2025 | Official leaderboard | |
| 12 | | 49.4% | Sep 2026 | Official leaderboard | |
| 13 | GPT-5.1 | 49.3% | thinking | Nov 2025 | Official leaderboard |
| 14 | GPT-5 | 49% | Aug 2025 | Official leaderboard | |
| 15 | | 48.9% | Sep 2026 | Official leaderboard | |
| 16 | o3 | 48.6% | Apr 2025 | Official leaderboard | |
| 17 | GPT-6 Astra | 48.4% | Sep 2026 | Official leaderboard | |
| 18 | GPT-6.1 Sol | 47.9% | Sep 2026 | Official leaderboard | |
| 19 | Grok 4.7 | 47.6% | xhigh | Sep 2026 | Official leaderboard |
| 20 | Claude Opus 5.5 | 47.6% | Sep 2026 | Official leaderboard | |
| 21 | | 47.3% | Oct 2026 | Official leaderboard | |
| 22 | GPT-5.2 Pro | 45.4% | Dec 2025 | Official leaderboard | |
| 23 | GPT-5.4 | 44.4% | high | Mar 2026 | Official leaderboard |
| 24 | Claude Opus 4.5 | 44.2% | thinking | Nov 2025 | Official leaderboard |
| 25 | | 44% | Feb 2026 | Official leaderboard | |
| 26 | Kimi K2.5 | 43.8% | Jan 2026 | Official leaderboard | |
| 27 | GPT-6 Luna | 42.9% | Sep 2026 | Official leaderboard | |
| 28 | | 41.4% | Jun 2025 | Official leaderboard | |
| 29 | | 41% | Jun 2025 | Official leaderboard | |
| 30 | Kimi K2 Thinking | 40.9% | Nov 2025 | Official leaderboard | |
| 31 | Claude Sonnet 4.5 | 40.8% | Sep 2025 | Official leaderboard | |
| 32 | | 40.6% | Nov 2025 | Official leaderboard | |
| 33 | gpt-oss-120b | 40.2% | Aug 2025 | Official leaderboard | |
| 34 | | 39.5% | Dec 2023 | Official leaderboard | |
| 35 | GPT-6 Sol | 38.6% | Sep 2026 | Official leaderboard | |
| 36 | | 38.3% | Jul 2025 | Official leaderboard | |
| 37 | o4-mini | 38.1% | Apr 2025 | Official leaderboard | |
| 38 | | 37.6% | Aug 2025 | Official leaderboard | |
| 39 | | 36.6% | May 2025 | Official leaderboard | |
| 40 | GPT-4.1 | 36.5% | Apr 2025 | Official leaderboard | |
| 41 | Kimi K2 Jul 2025 | 36.4% | Jul 2025 | Official leaderboard | |
| 42 | Claude Opus 4.1 | 34% | Aug 2025 | Official leaderboard | |
| 43 | GPT-4.1 mini | 30.4% | Apr 2025 | Official leaderboard | |
| 44 | | 24.8% | Apr 2025 | Official leaderboard |
Labs report scores for 1 model in their cards. We plot every source in the charts below.
6 models have held number one since its release in Nov 2025
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.
One lab put it in a launch post in the last six months
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 3 Aug 2026 | 58.3 | PRBench-Finance | |
| 3 Aug 2026 | 57.6 | PRBench-Legal |
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