MMSI-Bench

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

🌐 Homepage | 🤗 Dataset | 📑 Paper | 💻 Code | 📖 arXiv

🔔News

🔥[2025-10-23]: We added the normalized human response time for each MMSI-Bench sample and its difficulty level to our dataset on Hugging Face.

🔥[2025-06-18]: MMSI-Bench has been supported in the LMMs-Eval repository.

✨[2025-06-11]: MMSI-Bench was used for evaluation in the experiments of VILASR.

🔥[2025-06-9]: MMSI-Bench has been supported in the 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(mmsi_bench)

After downloading the parquet file, read each record, decode images from binary, and save them as JPG files.

import pandas as pd
import os

df = pd.read_parquet('MMSI_Bench.parquet')

output_dir = './images'
os.makedirs(output_dir, exist_ok=True)

for idx, row in df.iterrows():
    id_val = row['id']
    images = row['images']  
    question_type = row['question_type']
    question = row['question']
    answer = row['answer']
    thought = row['thought']

    image_paths = []
    if images is not None:
        for n, img_data in enumerate(images):
            image_path = f"{output_dir}/{id_val}_{n}.jpg"
            with open(image_path, "wb") as f:
                f.write(img_data)
            image_paths.append(image_path)
    else:
        image_paths = []

    print(f"id: {id_val}")
    print(f"images: {image_paths}")
    print(f"question_type: {question_type}")
    print(f"question: {question}")
    print(f"answer: {answer}")
    print(f"thought: {thought}")
    print("-" * 50)

Evaluation

Please refer to the evaluation guidelines of VLMEvalKit

🏆 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, nuScenes, Matterport3D, Ego4D, AgiBot-World, DTU, DAVIS-2017 ,and Waymo. We thank these teams for their open-source contributions.

Contact

• Sihan Yang: sihany077@gmail.com
• Runsen Xu: runsxu@gmail.com

Citation

@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}
}