Datasets:

rbler
/

MMSI-Video-Bench

🎥 Video Data (videos.zip): Contains the video clip file (.mp4) corresponding to each sample. This file is generally not required for most models.

🎥 Frame Data (frames.zip): Contains the frames (.jpg) extracted from each sample's video at the base sampling rate. This rate ensures no key information loss during sampling. Each frame file is named using the format {timestamp}_frame_{base_interval}_{image_id} (e.g., 00:06.00_frame_1.50_4), where the timestamp, also shown on the top-left corner of the frame, indicates its capture time in the original recording.

🖼️ Reference Image Data (ref_images.zip): Contains the auxiliary images referenced in the questions for each sample.

📝 Text Annotation (mmsivideo.json)：This file contains the annotation information for MMSI-Video-Bench. All time references in the questions correspond to the capture time in the original recording and align with the timestamp flag on each frame. Key fields include:

{
  "ref_images": [Paths to auxiliary images referenced in the question,...],
  "video_list": [
    {
      "path": Video clip file path,
      "start": Timestamp (in seconds) of the first frame of the video clip in the original recording,
      "end": Timestamp (in seconds) of the last frame of the video clip in the original recording,
      "base_fps": Base sampling rate
    },
    ...
  ],
  "frames_list": [[Paths to frames sampled at the base sampling rate,...],...],
  "system_prompt": "...",
  "task_prompt": Task-specific prompt,
  "user_prompt": Question text, with <video> as a placeholder for video and <image> for auxiliary images,
  "format_prompt": Output format requirements,
  "ground_truth": Correct answer
}

Unless otherwise specified, the model input generally consists of: system_prompt + task_prompt + user_prompt + format_prompt.

🚀 Evaluation

Please refer to the evaluation guidelines in our github repo.

🏆 Leaderboard

📦 Uniform-50 Setting

Model	Avg.(%)	Type
Human	96.40	Baseline
🥇Gemini 3 pro	37.97	Proprietary
🥈 O3	36.98	Proprietary
🥉GPT-5	36.80	Proprietary
Gemini 2.5 Flash	35.44	Proprietary
Gemini 2.5 Flash (Thinking)	35.17	Proprietary
Seed-1.6-vision	34.87	Proprietary
Claude-haiku-4.5	34.27	Proprietary
O4-mini	34.18	Proprietary
QwenVL2.5-72B	32.73	Open-Source
InternVL3-78B	32.55	Open-Source
Doubao-1.5-thinking	31.65	Proprietary
GPT-4o	31.56	Proprietary
InternVL2.5-78B	31.37	Open-Source
InternVL2.5-38B	31.01	Open-Source
QwenVL3-30B (Thinking)	30.83	Open-Source
LLaVA-Video-72B	30.38	Open-Source
InternVL3-8B	30.38	Open-Source
QwenVL2.5-VL-7B-Instruct	29.66	Open-Source
InternVL2.5-8B	29.11	Open-Source
InternVL3-38B	28.84	Open-Source
QwenVL3-30B	28.75	Open-Source
QwenVL2.5-32B	28.57	Open-Source
LLaVA-Video-7B	28.48	Open-Source
QwenVL3-8B	27.58	Open-Source
InternVideo2.5-8B	27.40	Open-Source
Random Guessing	24.10	Baseline

📦 Sufficient-Coverage Setting

Model	Avg.(%)	Type
Human	96.4	Baseline
🥇O3	37.34	Proprietary
🥈Gemini 2.5 Flash (Thinking)	36.71	Proprietary
🥉Gemini 2.5 Flash	36.62	Proprietary
O4-mini	35.08	Proprietary
QwenVL2.5-32B	32.37	Open-Source
QwenVL2.5-72B	31.83	Open-Source
InternVL3-8B	29.57	Open-Source
QwenVL3-30B	29.11	Open-Source
QwenVL3-8B	29.09	Open-Source
QwenVL2.5-7B	28.84	Open-Source
InternVL2.5-8B	28.66	Open-Source
GPT-4o	28.12	Proprietary
QwenVL3-30B (Thinking)	28.03	Open-Source
InternVideo2.5-8B	26.85	Open-Source
Random Guessing	24.10	Baseline

🤖 Robot Sub-bench

Model	Avg.(%)	Type
🥇Gemini 3 Pro	40.20	Proprietary
🥈Gemini 2.5 Flash (Thinking)	39.71	Proprietary
🥉Seed-1.6-vision	39.34	Proprietary
O3	39.22	Proprietary
QwenVL2.5-72B	37.75	Open-Source
InternVL3-8B	37.75	Open-Source
GPT-5	37.75	Proprietary
InternVL2.5-38B	36.27	Open-Source
Doubao-1.5-thinking	36.07	Proprietary
Gemini 2.5 Flash	35.78	Proprietary
O4-mini	35.29	Proprietary
QwenVL2.5-7B	34.8	Open-Source
InternVL2.5-78B	34.8	Open-Source
Claude-haiku-4.5	34.8	Proprietary
InternVL3-78B	34.31	Open-Source
LLaVA-Video-72B	34.31	Open-Source
QwenVL3-30B	32.84	Open-Source
QwenVL2.5-32B	32.84	Open-Source
QwenVL3-8B	32.12	Open-Source
InternVideo2.5-8B	29.90	Open-Source
GPT-4o	29.90	Proprietary
InternVL2.5-8B	28.43	Open-Source
InternVL3-38B	27.94	Open-Source
QwenVL3-30B (Thinking)	27.94	Open-Source
LLaVA-Video-7B	24.51	Open-Source

🏠 Indoor Scene Perception Sub-bench

Model	Avg.(%)	Type
🥇GPT-5	41.68	Proprietary
🥈O3	40.73	Proprietary
🥉Gemini 2.5 Flash	39.39	Proprietary
Gemini 3 Pro	39.39	Proprietary
Gemini 2.5 Flash (Thinking)	37.86	Proprietary
O4-mini	37.48	Proprietary
Seed-1.6-vision	34.2	Proprietary
Claude-haiku-4.5	33.46	Proprietary
Doubao-1.5-thinking	33.04	Proprietary
InternVL3-78B	32.5	Open-Source
QwenVL3-30B (Thinking)	32.31	Open-Source
GPT-4o	31.74	Proprietary
QwenVL2.5-72B	30.78	Open-Source
InternVL2.5-78B	30.4	Open-Source
QwenVL3-30B	30.02	Open-Source
QwenVL2.5-32B	29.64	Open-Source
InternVL2.5-8B	29.45	Open-Source
InternVL3-38B	29.06	Open-Source
QwenVL3-8B	28.68	Open-Source
InternVL2.5-38B	28.3	Open-Source
LLaVA-Video-72B	28.11	Open-Source
InternVL3-8B	27.72	Open-Source
LLaVA-Video-7B	27.53	Open-Source
QwenVL2.5-7B	27.15	Open-Source
InternVideo2.5-8B	26.77	Open-Source

📍 Grounding Sub-bench

Model	Avg.(%)	Type
🥇Gemini 2.5 Flash	38.81	Proprietary
🥈Gemini 2.5 Flash (Thinking)	38.21	Proprietary
🥉O3	37.61	Proprietary
Doubao-1.5-thinking	37.05	Proprietary
InternVL3-78B	35.52	Open-Source
GPT-5	35.22	Proprietary
Gemini 3 Pro	35.22	Proprietary
O4-mini	34.33	Proprietary
QwenVL2.5-72B	34.33	Open-Source
Seed-1.6-vision	33.04	Proprietary
Claude-haiku-4.5	32.84	Proprietary
InternVL2.5-38B	31.94	Open-Source
InternVL3-8B	31.94	Open-Source
GPT-4o	31.94	Proprietary
QwenVL3-30B (Thinking)	31.64	Open-Source
QwenVL2.5-32B	31.04	Open-Source
LLaVA-Video-72B	31.04	Open-Source
InternVL3-38B	30.45	Open-Source
InternVL2.5-8B	30.15	Open-Source
InternVL2.5-78B	29.85	Open-Source
QwenVL3-30B	29.25	Open-Source
QwenVL2.5-7B	28.66	Open-Source
QwenVL3-8B	28.66	Open-Source
InternVideo2.5-8B	27.76	Open-Source
LLaVA-Video-7B	27.16	Open-Source

Note: For the three sub-benchmarks, we take the higher score of each model across the two settings for easier presentation.

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