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  # MMSI-Bench
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  This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence]"
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- [**🌐 Homepage**](https://runsenxu.com/projects/MMSI_Bench/) | [**πŸ€— Dataset**](https://huggingface.co/datasets/RunsenXu/MMSI-Bench) | [**πŸ“‘ Paper**] | [**πŸ’» Code**](https://github.com/OpenRobotLab/MMSI_Bench/tree/main) | [**πŸ“– arXiv**] |
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  ## πŸ””News
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- **πŸ”₯[2025-05-31]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.**
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  **πŸ”₯[2025-05-30]: We released the ArXiv paper.**
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@@ -14,7 +14,7 @@ This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for M
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  ```
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  from datasets import load_dataset
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- vsi_bench = load_dataset("RunsenXu/MMSI-Bench")
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  print(dataset)
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  ```
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@@ -34,37 +34,35 @@ Please refer to the [evaluation guidelines](https://github.com/open-compass/VLME
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  | Gemini-2.5-Pro--Thinking | 37.0 | Proprietary |
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  | Gemini-2.5-Pro | 36.9 | Proprietary |
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  | Doubao-1.5-pro | 33.0 | Proprietary |
 
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  | Qwen2.5-VL-72B | 30.7 | Open-source |
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  | NVILA-15B | 30.5 | Open-source |
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- | GPT-4.1 | 30.9 | Proprietary |
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  | GPT-4o | 30.3 | Proprietary |
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  | Claude-3.7-Sonnet--Thinking | 30.2 | Proprietary |
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  | Seed1.5-VL | 29.7 | Proprietary |
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- | DeepSeek-VL2-Small | 28.6 | Open-source |
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  | InternVL2.5-8B | 28.7 | Open-source |
 
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  | InternVL3-78B | 28.5 | Open-source |
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  | InternVL2.5-78B | 28.5 | Open-source |
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  | LLaVA-OneVision-72B | 28.4 | Open-source |
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- | InternVL2.5-2B | 29.0 | Open-source |
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- | InternVL2.5-26B | 28.0 | Open-source |
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  | NVILA-8B | 28.1 | Open-source |
 
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  | DeepSeek-VL2 | 27.1 | Open-source |
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  | InternVL3-1B | 27.0 | Open-source |
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  | InternVL3-9B | 26.7 | Open-source |
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  | Qwen2.5-VL-3B | 26.5 | Open-source |
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  | InternVL2.5-1B | 26.1 | Open-source |
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  | InternVL2.5-4B | 26.3 | Open-source |
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- | InternVL3-8B | 25.7 | Open-source |
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  | Qwen2.5-VL-7B | 25.9 | Open-source |
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- | InternVL3-2B | 25.3 | Open-source |
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  | Llama-3.2-11B-Vision | 25.4 | Open-source |
 
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  | πŸƒ **Random Guessing** | 25.0 | Baseline |
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  | LLaVA-OneVision-7B | 24.5 | Open-source |
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  | DeepSeek-VL2-Tiny | 24.0 | Open-source |
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  | Blind GPT-4o | 22.7 | Baseline |
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-
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-
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  ## Acknowledgment
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  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.
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@@ -73,8 +71,11 @@ MMSI-Bench makes use of data from existing image datasets: [ScanNet](http://www.
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  - Runsen Xu: runsxu@gmail.com
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  ## Citation
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-
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- **BibTeX:**
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  ```bibtex
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-
 
 
 
 
 
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  ```
 
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  # MMSI-Bench
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  This repo contains evaluation code for the paper "[MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence]"
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+ [**🌐 Homepage**](https://runsenxu.com/projects/MMSI_Bench/) | [**πŸ€— Dataset**](https://huggingface.co/datasets/RunsenXu/MMSI-Bench) | [**πŸ“‘ Paper**](https://arxiv.org/pdf/2505.23764) | [**πŸ’» Code**](https://github.com/OpenRobotLab/MMSI-Bench) | [**πŸ“– arXiv**](https://arxiv.org/abs/2505.23764)
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  ## πŸ””News
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+ <!-- **πŸ”₯[2025-05-31]: MMSI-Bench has been supported in the [VLMEvalKit](https://github.com/open-compass/VLMEvalKit) repository.** -->
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  **πŸ”₯[2025-05-30]: We released the ArXiv paper.**
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  ```
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  from datasets import load_dataset
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+ mmsi_bench = load_dataset("RunsenXu/MMSI-Bench")
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  print(dataset)
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  ```
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  | Gemini-2.5-Pro--Thinking | 37.0 | Proprietary |
35
  | Gemini-2.5-Pro | 36.9 | Proprietary |
36
  | Doubao-1.5-pro | 33.0 | Proprietary |
37
+ | GPT-4.1 | 30.9 | Proprietary |
38
  | Qwen2.5-VL-72B | 30.7 | Open-source |
39
  | NVILA-15B | 30.5 | Open-source |
 
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  | GPT-4o | 30.3 | Proprietary |
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  | Claude-3.7-Sonnet--Thinking | 30.2 | Proprietary |
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  | Seed1.5-VL | 29.7 | Proprietary |
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+ | InternVL2.5-2B | 29.0 | Open-source |
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  | InternVL2.5-8B | 28.7 | Open-source |
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+ | DeepSeek-VL2-Small | 28.6 | Open-source |
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  | InternVL3-78B | 28.5 | Open-source |
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  | InternVL2.5-78B | 28.5 | Open-source |
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  | LLaVA-OneVision-72B | 28.4 | Open-source |
 
 
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  | NVILA-8B | 28.1 | Open-source |
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+ | InternVL2.5-26B | 28.0 | Open-source |
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  | DeepSeek-VL2 | 27.1 | Open-source |
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  | InternVL3-1B | 27.0 | Open-source |
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  | InternVL3-9B | 26.7 | Open-source |
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  | Qwen2.5-VL-3B | 26.5 | Open-source |
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  | InternVL2.5-1B | 26.1 | Open-source |
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  | InternVL2.5-4B | 26.3 | Open-source |
 
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  | Qwen2.5-VL-7B | 25.9 | Open-source |
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+ | InternVL3-8B | 25.7 | Open-source |
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  | Llama-3.2-11B-Vision | 25.4 | Open-source |
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+ | InternVL3-2B | 25.3 | Open-source |
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  | πŸƒ **Random Guessing** | 25.0 | Baseline |
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  | LLaVA-OneVision-7B | 24.5 | Open-source |
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  | DeepSeek-VL2-Tiny | 24.0 | Open-source |
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  | Blind GPT-4o | 22.7 | Baseline |
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  ## Acknowledgment
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  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.
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  - Runsen Xu: runsxu@gmail.com
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  ## Citation
 
 
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  ```bibtex
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+ @article{yang2025mmsi,
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+ title={MMSI-Bench: A Benchmark for Multi-Image Spatial Intelligence},
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+ 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},
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+ journal={arXiv preprint arXiv:2505.23764},
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+ year={2025}
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+ }
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  ```