Measuring Multimodal Mathematical Reasoning with the MATH-Vision🔥 Dataset

🌟 This is the official repository for the paper "Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset", which contains both evaluation code and data for the MATH-V benchmark.

[🌐 Homepage] [🤗 Huggingface Dataset] [📊 Leaderboard ] [📊 Open Source Leaderboard ] [📖 Paper]

💥 News

• [2025.05.16] 💥 We now support the official open-source leaderboard! 🔥🔥🔥 Skywork-R1V2-38B is the best open-source model, scoring 49.7% on MATH-Vision. 🔥🔥🔥 MathCoder-VL-2B is the best small model on MATH-Vision, scoring 21.7%. See the full open-source leaderboard.
• [2025.05.16] 🤗 MathCoder-VL-2B, MathCoder-VL-8B and FigCodifier-8B is available now! 🔥🔥🔥
• [2025.05.16] Our MathCoder-VL is accepted to ACL 2025 Findings. 🔥🔥🔥
• [2025.05.13] 🔥🔥🔥 Seed1.5-VL achieves 68.7% on MATH-Vision! 🎉 Congratulations!
• [2025.04.11] 💥 Kimi-VL-A3B-Thinking achieves strong multimodal reasoning with just 2.8B LLM activated parameters! Congratulations! See the full leaderboard.
• [2025.04.10] 🔥🔥🔥 SenseNova V6 Reasoner achieves 55.39% on MATH-Vision! 🎉 Congratulations!
• [2025.04.05] 💥 Step R1-V-Mini 🥇 Sets New SOTA on MATH-V with 56.6%! See the full leaderboard.
• [2025.03.10] 💥 Kimi k1.6 Preview Sets New SOTA on MATH-V with 53.29%! See the full leaderboard.
• [2025.02.28] 💥 Doubao-1.5-pro Sets New SOTA on MATH-V with 48.62%! Read more on the Doubao blog.
• [2025.01.26] 🚀 Qwen2.5-VL-72B achieves 38.1%, establishing itself as the best-performing one in open-sourced models. 🎉 Congratulations!
• [2025.01.22] 💥 Kimi k1.5 achieves new SOTA on MATH-Vision with 38.6%! Learn more at the Kimi k1.5 report.
• [2024-09-27] MATH-V is accepted by NeurIPS DB Track, 2024! 🎉🎉🎉
• [2024-08-29] 🔥🔥🔥 Qwen2-VL-72B achieves new open-sourced SOTA on MATH-Vision with 25.9! 🎉 Congratulations! Learn more at the Qwen2-VL blog.
• [2024-07-19] open-compass/VLMEvalKit now supports MATH-V, utilizing LLMs for more accurate answer extraction!🔥🔥
• [2024-05-19] OpenAI's GPT-4o scores 30.39% on MATH-V, considerable advancement in short time! 💥
• [2024-03-01] InternVL-Chat-V1-2-Plus achieves 16.97%, establishing itself as the new best-performing open-sourced model. 🎉 Congratulations!
• [2024-02-23] Our dataset is now accessible at huggingface.
• [2024-02-22] The top-performing model, GPT-4V only scores 23.98% on MATH-V, while human performance is around 70%.
• [2024-02-22] Our paper is now accessible at ArXiv Paper.

👀 Introduction

Recent advancements in Large Multimodal Models (LMMs) have shown promising results in mathematical reasoning within visual contexts, with models approaching human-level performance on existing benchmarks such as MathVista. However, we observe significant limitations in the diversity of questions and breadth of subjects covered by these benchmarks. To address this issue, we present the MATH-Vision (MATH-V) dataset, a meticulously curated collection of 3,040 high-quality mathematical problems with visual contexts sourced from real math competitions. Spanning 16 distinct mathematical disciplines and graded across 5 levels of difficulty, our dataset provides a comprehensive and diverse set of challenges for evaluating the mathematical reasoning abilities of LMMs.

The accuracies of four prominent Large Multimodal Models (LMMs), random chance, and human performance are evaluated on our proposed MATH-Vision (MATH-V) across 16 subjects.

Through extensive experimentation, we unveil a notable performance gap between current LMMs and human performance on MATH-V, underscoring the imperative for further advancements in LMMs.

You can refer to our project homepage and the paper for more details.

📐 Dataset Examples

Some examples of MATH-V on three subjects: analytic geometry, topology, and graph theory.

Analytic geometry

Topology

Graph Geometry

🏆 Leaderboard

The leaderboard is available here.

📈 Evaluation

Generating Outputs of Different Models

Gemini

python models/Gemini.py --in_path ./data/test.jsonl --save_path ./Gemini.jsonl

This will run the Gemini API and save the outputs to ./Gemini.jsonl path. You can modify the system prompt, max tokens, etc. in the benchmark_gemini function.

GPT_with_caption

Generate image captions using GPT-4V:

python models/GPT_with_caption.py --model gpt-4-vision-preview --in_path ./data/test.jsonl --save_path ./data/gpt4v-captions.jsonl

Generate outputs using ChatGPT-3.5 or GPT-4 with image captions:

python models/GPT_with_caption.py --model gpt-3.5-turbo-0125 (gpt-4-turbo-preview) --in_path ./data/test.jsonl --save_path ./gpt3.5_caption.jsonl (./gpt4_caption.jsonl)

Evaluation of Model Outputs

Once all the model outputs have been generated, execute the python evaluation/evaluate.py function to assess these outputs. This script will examine all outputs located in the outputs/ directory, computing overall accuracy as well as accuracy for each subject and level.

📝 Citation

If you find this benchmark useful in your research, please consider citing this BibTex:

@inproceedings{
wang2024measuring,
title={Measuring Multimodal Mathematical Reasoning with MATH-Vision Dataset},
author={Ke Wang and Junting Pan and Weikang Shi and Zimu Lu and Houxing Ren and Aojun Zhou and Mingjie Zhan and Hongsheng Li},
booktitle={The Thirty-eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2024},
url={https://openreview.net/forum?id=QWTCcxMpPA}
}

@inproceedings{
wang2025mathcodervl,
title={MathCoder-{VL}: Bridging Vision and Code for Enhanced Multimodal Mathematical Reasoning},
author={Ke Wang and Junting Pan and Linda Wei and Aojun Zhou and Weikang Shi and Zimu Lu and Han Xiao and Yunqiao Yang and Houxing Ren and Mingjie Zhan and Hongsheng Li},
booktitle={The 63rd Annual Meeting of the Association for Computational Linguistics},
year={2025},
url={https://openreview.net/forum?id=nuvtX1imAb}
}

🧠 Related Work

• [CSV🔥] Solving Challenging Math Word Problems Using GPT-4 Code Interpreter with Code-based Self-Verification
• [MathGenie] MathGenie: Generating Synthetic Data with Question Back-translation for Enhancing Mathematical Reasoning of LLMs
• [MathCoder🔥] MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning
• [MathVerse] MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?
• [MathVista] MathVista: Evaluating Mathematical Reasoning of Foundation Models in Visual Contexts
• [SPHINX] The Joint Mixing of Weights, Tasks, and Visual Embeddings for Multi-modal LLMs
• [SPHINX-X] Scaling Data and Parameters for a Family of Multi-modal Large Language Models