IMProofBench
Agents with web search and SageMath write proofs for private PhD-level problems written by mathematicians. Experts grade the proofs, and final-answer subquestions are graded automatically.
NeurIPS 2025. The July 2026 paper v2 grew the set from 39 to 77 problems and 14 models, so scores from the two versions do not compare.
- Released
- September 2025
- Kind
- Research mathematics
- Level
- Research frontier
- Scored
- Graded by people
- Answers
- Written proof
- Tasks
- Kept private
- Strongest model tested
- GPT-5.4 #27 on the capability index
- Last model added
- March 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
As the mathematical capabilities of large language models (LLMs) improve, it becomes increasingly important to evaluate their performance on research-level tasks at the frontier of mathematical knowledge. However, existing benchmarks are limited, as they focus solely on final-answer questions or high-school competition problems. To address this gap, we introduce IMProofBench, a private benchmark consisting of 77 peer-reviewed problems developed by expert mathematicians. Each problem requires a detailed proof and is paired with subproblems that have final answers, supporting both an evaluation by human experts and a large-scale quantitative analysis through automated grading. Furthermore, unlike prior benchmarks, the evaluation setup simulates a realistic research environment: models operate in an agentic framework with tools like web search for literature review and mathematical software such as SageMath. Our results show that current LLMs can already solve a significant percentage of research-level questions. IMProofBench will continue to evolve as a dynamic benchmark in collaboration with the mathematical community, ensuring its relevance for evaluating the next generation of LLMs.
GPT-5.4 leads with 49.3% among models released by March 2026
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders.
The newest model with a score here came out in March 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 16 more
- 1 GPT-5.4 49.3%
- 2
Gemini 3.1 Pro 45.7%
- 3 GPT-5.2 Pro web search 36.9%
- 4 GPT-5.2 32.4%
- 5 Claude Opus 4.6 31.7%
- 6 GPT-5 20.4%
- 7 Grok 4 11.1%
- 8 o4-mini 6.45%
- 9 Gemini 2.5 Pro (snapshot unspecified) 6.08%
- 10 Claude Opus 4.1 3.38%
Source: Official IMProofBench leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-5.4 | 49.3% | inspect | Mar 2026 | Official leaderboard |
| 2 | | 45.7% | inspect | Feb 2026 | Official leaderboard |
| 3 | GPT-5.2 Pro | 36.9% | web search, non-agentic | Dec 2025 | Official leaderboard |
| 4 | GPT-5.2 | 32.4% | inspect | Dec 2025 | Official leaderboard |
| 5 | Claude Opus 4.6 | 31.7% | inspect | Feb 2026 | Official leaderboard |
| 6 | GPT-5 | 20.4% | inspect | Aug 2025 | Official leaderboard |
| 7 | Grok 4 | 11.1% | inspect | Jul 2025 | Official leaderboard |
| 8 | o4-mini | 6.45% | inspect | Apr 2025 | Official leaderboard |
| 9 | Gemini 2.5 Pro (snapshot unspecified) | 6.08% | inspect | Official leaderboard | |
| 10 | Claude Opus 4.1 | 3.38% | inspect | Aug 2025 | Official leaderboard |
4 models have held number one on the official leaderboard since its release in September 2025
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official IMProofBench 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 IMProofBench 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.
More in Math
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- IMO-AnswerBench In use 400 olympiad problems with short verifiable answers, robustified against memorization. Scored by answer match. GLM-5.2 91%
- HMMT 2026 In use Models solve the 33 final-answer problems from the February 2026 Harvard-MIT Mathematics Tournament. Scored by exact answer match. GPT-5.5 98.5%
- PolyMath In use Models solve 9,000 parallel math problems in 18 languages across four difficulty levels, from school math to frontier math. Final answers are checked.
Qwen3.7-Max 86.5%
- MathArena (Apex, IMO, USAMO) In use Evaluates models on newly released math competitions after release. Final-answer or human/LLM-judged proof scores. Claude Opus 4.8 81.3%
- MathArena Apex Shortlist In use Models solve 47 hard final-answer problems that MathArena selected from 2025 competitions, where the best models scored about 50% at selection. Scored by accuracy over repeated attempts. GPT-5.5 98.4%