MathBench
Models answer 3,709 math questions in five stages from basic arithmetic to college, in Chinese and English. Each stage also tests theory knowledge, and Circular Evaluation reshuffles answer options to check consistency.
Accepted to ACL 2024 Findings. The repository has about 118 stars.
- Released
- May 2024
- Kind
- Word problems
- Level
- University
- Scored
- Checked automatically
- Answers
- Multiple choice
- Tasks
- All public
- Strongest model tested
- GPT-4o May 2024 #161 on the capability index
- Last model added
- June 2024
- Scores refreshed
- November 12th, 2026
In the authors’ words
Recent advancements in large language models (LLMs) have showcased significant improvements in mathematics. However, traditional math benchmarks like GSM8k offer a unidimensional perspective, falling short in providing a holistic assessment of the LLMs' math capabilities. To address this gap, we introduce MathBench, a new benchmark that rigorously assesses the mathematical capabilities of large language models. MathBench spans a wide range of mathematical disciplines, offering a detailed evaluation of both theoretical understanding and practical problem-solving skills. The benchmark progresses through five distinct stages, from basic arithmetic to college mathematics, and is structured to evaluate models at various depths of knowledge. Each stage includes theoretical questions and application problems, allowing us to measure a model's mathematical proficiency and its ability to apply concepts in practical scenarios. MathBench aims to enhance the evaluation of LLMs' mathematical abilities, providing a nuanced view of their knowledge understanding levels and problem solving skills in a bilingual context. The project is released at https://github.com/open-compass/MathBench .
GPT-4o leads with 70.9% among models released by June 2024
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 June 2024, 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
- 70.9 GPT-4o
- 67.0
Qwen2-72B
- 63.0 Claude 3 Opus
- 59.9
DeepSeek-V2 (MoE-236B, May 2024)
- 59.2 Qwen-Max-0428
- 58.8 GPT-4 Turbo (Jan 2024)
- 58.4
Qwen1.5-110B
- 56.4
Llama 3-70B
- 53.4
Qwen2-7B-Instruct
- 52.2
Yi-1.5-34B
- 51.3 GLM-4
- 51.1
Yi-1.5-9B-Chat
- 50.3 DeepSeek-Math-7B-RL
- 49.7 Qwen-72B-Chat
- 47.7 GLM-4-9B-Chat
- 42.1 InternLM2-Chat-20B
- 41.9 InternLM2-Chat-Math-20B
- 40.9
DeepSeekMath-7B-Instruct
- 40.3
DeepSeek LLM 67B
- 39.5
Qwen-14B
- 37.7 InternLM2-Chat-Math-7B
- 36.7
Llama 3-8B
- 34.1 InternLM2-Chat-7B
- 31.6
Yi-34B
- 27.7
Qwen-7B
- 24.9 Metamath-Llemma-7B
- 24.7 MammoTH-70B
- 22.3 DeepSeek-7B-Chat
- 20.9 Baichuan2-13B-Chat
- 19.9 ChatGLM3-6B
- 18.5 MammoTH-13B
- 17.2
Yi-6B-Chat
- 11.3 MammoTH-7B
- 1 GPT-4o May 2024 70.9%
- 2
Qwen2-72B 67%
- 3 Claude 3 Opus 63%
- 4
DeepSeek-V2 (MoE-236B, May 2024) 59.9%
- 5 Qwen-Max-0428 59.2%
- 6 GPT-4 Turbo (Jan 2024) 58.8%
- 7
Qwen1.5-110B 58.4%
- 8
Llama 3-70B 56.4%
- 9
Qwen2-7B-Instruct 53.4%
- 10
Yi-1.5-34B 52.2%
- 11 GLM-4 51.3%
- 12
Yi-1.5-9B-Chat 51.1%
- 13 DeepSeek-Math-7B-RL 50.3%
- 14 Qwen-72B-Chat 49.7%
- 15 GLM-4-9B-Chat 47.7%
- 16 InternLM2-Chat-20B 42.1%
- 17 InternLM2-Chat-Math-20B 41.9%
- 18
DeepSeekMath-7B-Instruct 40.9%
- 19
DeepSeek LLM 67B 40.3%
- 20
Qwen-14B 39.5%
- 21 InternLM2-Chat-Math-7B 37.7%
- 22
Llama 3-8B 36.7%
- 23 InternLM2-Chat-7B 34.1%
- 24
Yi-34B 31.6%
- 25
Qwen-7B 27.7%
- 26 Metamath-Llemma-7B 24.9%
- 27 MammoTH-70B 24.7%
- 28 DeepSeek-7B-Chat 22.3%
- 29 Baichuan2-13B-Chat 20.9%
- 30 ChatGLM3-6B 19.9%
- 31 MammoTH-13B 18.5%
- 32
Yi-6B-Chat 17.2%
- 33 MammoTH-7B 11.3%
Source: Official MathBench leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | GPT-4o May 2024 | 70.9% | May 2024 | Official leaderboard | |
| 2 | | 67% | Jun 2024 | Official leaderboard | |
| 3 | Claude 3 Opus | 63% | Feb 2024 | Official leaderboard | |
| 4 | | 59.9% | May 2024 | Official leaderboard | |
| 5 | Qwen-Max-0428 | 59.2% | Official leaderboard | ||
| 6 | GPT-4 Turbo (Jan 2024) | 58.8% | Jan 2024 | Official leaderboard | |
| 7 | | 58.4% | Apr 2024 | Official leaderboard | |
| 8 | | 56.4% | Apr 2024 | Official leaderboard | |
| 9 | | 53.4% | Jun 2024 | Official leaderboard | |
| 10 | | 52.2% | May 2024 | Official leaderboard | |
| 11 | GLM-4 | 51.3% | Jan 2024 | Official leaderboard | |
| 12 | | 51.1% | May 2024 | Official leaderboard | |
| 13 | DeepSeek-Math-7B-RL | 50.3% | Official leaderboard | ||
| 14 | Qwen-72B-Chat | 49.7% | Official leaderboard | ||
| 15 | GLM-4-9B-Chat | 47.7% | Jun 2024 | Official leaderboard | |
| 16 | InternLM2-Chat-20B | 42.1% | Official leaderboard | ||
| 17 | InternLM2-Chat-Math-20B | 41.9% | Official leaderboard | ||
| 18 | | 40.9% | Feb 2024 | Official leaderboard | |
| 19 | | 40.3% | Nov 2023 | Official leaderboard | |
| 20 | | 39.5% | Sep 2023 | Official leaderboard | |
| 21 | InternLM2-Chat-Math-7B | 37.7% | Official leaderboard | ||
| 22 | | 36.7% | Apr 2024 | Official leaderboard | |
| 23 | InternLM2-Chat-7B | 34.1% | Official leaderboard | ||
| 24 | | 31.6% | Nov 2023 | Official leaderboard | |
| 25 | | 27.7% | Sep 2023 | Official leaderboard | |
| 26 | Metamath-Llemma-7B | 24.9% | Official leaderboard | ||
| 27 | MammoTH-70B | 24.7% | Official leaderboard | ||
| 28 | DeepSeek-7B-Chat | 22.3% | Official leaderboard | ||
| 29 | Baichuan2-13B-Chat | 20.9% | Sep 2023 | Official leaderboard | |
| 30 | ChatGLM3-6B | 19.9% | Oct 2023 | Official leaderboard | |
| 31 | MammoTH-13B | 18.5% | Official leaderboard | ||
| 32 | | 17.2% | Nov 2023 | Official leaderboard | |
| 33 | MammoTH-7B | 11.3% | Official leaderboard |
2 models have held number one on the official leaderboard since its release in May 2024
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official MathBench 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 MathBench 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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