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sihany
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MMSI-Bench-test

Tasks:
Question Answering
Visual Question Answering
Multiple Choice
Modalities:
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Size:
1K - 10K
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MMSI-Bench-test
File size: 4,722 Bytes
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---
language:
- en
license: cc-by-4.0
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- visual-question-answering
- multiple-choice
pretty_name: MMSI-Bench
dataset_info:
  features:
  - name: id
    dtype: int64
  - name: images
    sequence: image
  - name: question_type
    dtype: string
  - name: question
    dtype: string
  - name: answer
    dtype: string
  - name: thought
    dtype: string
  splits:
    - name: test
      num_examples: 1000

configs:
  - config_name: default
    data_files:
      - split: test
        path: MMSI_Bench.parquet
---

# MMSI-Bench
This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence]" 

[**🌐 Homepage**](https://runsenxu.com/projects/MMSI_Bench/) | [**🤗 Dataset**](https://huggingface.co/datasets/RunsenXu/MMSI-Bench) | [**📑 Paper**](https://arxiv.org/pdf/2505.23764) | [**💻 Code**](https://github.com/OpenRobotLab/MMSI-Bench) | [**📖 arXiv**](https://arxiv.org/abs/2505.23764)



## 🔔News
  <!-- **🔥[2025-05-31]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.** -->

 **🔥[2025-05-30]: We released the ArXiv paper.**

 ## Load Dataset
```
from datasets import load_dataset

mmsi_bench = load_dataset("RunsenXu/MMSI-Bench")
print(dataset)
```


## Evaluation
Please refer to the [evaluation guidelines](https://github.com/open-compass/VLMEvalKit/blob/main/docs/en/Quickstart.md) of [VLMEvalKit](https://github.com/open-compass/VLMEvalKit)
 
<!-- <img src="assets/radar_v1.png" width="400" /> -->

## 🏆 MMSI-Bench Leaderboard

| Model                        | Avg. (%) | Type         |
|------------------------------|:--------:|:-------------|
| 🥇 **Human Level**           | 97.2     | Baseline     |
| 🥈 o3                        | 41.0     | Proprietary  |
| 🥉 GPT-4.5                   | 40.3     | Proprietary  |
| Gemini-2.5-Pro--Thinking     | 37.0     | Proprietary  |
| Gemini-2.5-Pro               | 36.9     | Proprietary  |
| Doubao-1.5-pro               | 33.0     | Proprietary  |
| GPT-4.1                      | 30.9     | Proprietary  |
| Qwen2.5-VL-72B               | 30.7     | Open-source  |
| NVILA-15B                    | 30.5     | Open-source  |
| GPT-4o                       | 30.3     | Proprietary  |
| Claude-3.7-Sonnet--Thinking  | 30.2     | Proprietary  |
| Seed1.5-VL                   | 29.7     | Proprietary  |
| InternVL2.5-2B               | 29.0     | Open-source  |
| InternVL2.5-8B               | 28.7     | Open-source  |
| DeepSeek-VL2-Small           | 28.6     | Open-source  |
| InternVL3-78B                | 28.5     | Open-source  |
| InternVL2.5-78B              | 28.5     | Open-source  |
| LLaVA-OneVision-72B          | 28.4     | Open-source  |
| NVILA-8B                     | 28.1     | Open-source  |
| InternVL2.5-26B              | 28.0     | Open-source  |
| DeepSeek-VL2                 | 27.1     | Open-source  |
| InternVL3-1B                 | 27.0     | Open-source  |
| InternVL3-9B                 | 26.7     | Open-source  |
| Qwen2.5-VL-3B                | 26.5     | Open-source  |
| InternVL2.5-1B               | 26.1     | Open-source  |
| InternVL2.5-4B               | 26.3     | Open-source  |
| Qwen2.5-VL-7B                | 25.9     | Open-source  |
| InternVL3-8B                 | 25.7     | Open-source  |
| Llama-3.2-11B-Vision         | 25.4     | Open-source  |
| InternVL3-2B                 | 25.3     | Open-source  |
| 🃏 **Random Guessing**        | 25.0     | Baseline     |
| LLaVA-OneVision-7B           | 24.5     | Open-source  |
| DeepSeek-VL2-Tiny            | 24.0     | Open-source  |
| Blind GPT-4o                 | 22.7     | Baseline     |

## Acknowledgment
MMSI-Bench makes use of data from existing image datasets: [ScanNet](http://www.scan-net.org/), [nuScenes](https://www.nuscenes.org/), [Matterport3D](https://niessner.github.io/Matterport/), [Ego4D](https://ego4d-data.org/), [AgiBot-World](https://agibot-world.cn/), [DTU](https://roboimagedata.compute.dtu.dk/?page_id=36), [DAVIS-2017](https://davischallenge.org/) ,and [Waymo](https://waymo.com/open/). We thank these teams for their open-source contributions.

## Contact
- Sihan Yang: sihany077@gmail.com
- Runsen Xu:  runsxu@gmail.com

## Citation
```bibtex
@article{yang2025mmsi,
  title={MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence},
  author={Yang, Sihan and Xu, Runsen and Xie, Yiman and Yang, Sizhe and Li, Mo and Lin, Jingli and Zhu, Chenming and Chen, Xiaochen and Duan, Haodong and Yue, Xiangyu and Lin, Dahua and Wang, Tai and Pang, Jiangmiao},
  journal={arXiv preprint arXiv:2505.23764},
  year={2025}
}
```