multimodal benchmark
Video-MME
Video-MME is the first-ever comprehensive evaluation benchmark of Multi-modal Large Language Models (MLLMs) in video analysis. It features 900 videos totaling 254 hours with 2,700 human-annotated question-answer pairs across 6 primary visual domains (Knowledge, Film & Television, Sports Competition, Life Record, Multilingual, and others) and 30 subfields. The benchmark evaluates models across diverse temporal dimensions (11 seconds to 1 hour), integrates multi-modal inputs including video frames, subtitles, and audio, and uses rigorous manual labeling by expert annotators for precise assessment.
Updated Aug 11, 2026
Models17
Model coverage17
MetricScore
EvidenceB
Video-MME Ranking
Higher score ranks better on this benchmark.
| 01 | BY | 89.2% | 100.0% | 17 | C | |
| 02 | BY | 89.0% | 93.8% | 17 | C | |
| 03 | AC | 88.0% | 87.5% | 17 | C | |
| 04 | XI | 87.7% | 81.3% | 17 | C | |
| 05 | MA | 87.4% | 75.0% | 17 | C | |
| 06 | MI | 85.4% | 68.8% | 17 | C | |
| 07 | GO | 84.8% | 62.5% | 17 | C | |
| 08 | AC | 84.2% | 56.3% | 17 | C | |
| 09 | GO | 78.6% | 50.0% | 17 | C | |
| 10 | AM Nova 2 OmniAmazon | 77.9% | 43.8% | 17 | C | |
| 11 | GO | 76.1% | 37.5% | 17 | C | |
| 12 | AC | 74.5% | 31.3% | 17 | C | |
| 13 | AC | 73.3% | 25.0% | 17 | C | |
| 14 | AC | 71.8% | 18.8% | 17 | C | |
| 15 | AC | 71.4% | 12.5% | 17 | C | |
| 16 | GO | 66.2% | 6.3% | 17 | C | |
| 17 | MI | 55.0% | 0.0% | 17 | C |
Video-MME Score Distribution
A closer view of the leading scores on this benchmark.
Video-MME Highlights
The leading models and scores on this benchmark.
What is Video-MME?
What Video-MME measures and how its scores work.
Video-MME is the first-ever comprehensive evaluation benchmark of Multi-modal Large Language Models (MLLMs) in video analysis. It features 900 videos totaling 254 hours with 2,700 human-annotated question-answer pairs across 6 primary visual domains (Knowledge, Film & Television, Sports Competition, Life Record, Multilingual, and others) and 30 subfields. The benchmark evaluates models across diverse temporal dimensions (11 seconds to 1 hour), integrates multi-modal inputs including video frames, subtitles, and audio, and uses rigorous manual labeling by expert annotators for precise assessment.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- Video-MME
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- video-mme|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about Video-MME.
Which model scores highest on Video-MME?
Seed 2.1 Pro is currently ranked first with 89.2%.
What does Video-MME measure?
Video-MME is the first-ever comprehensive evaluation benchmark of Multi-modal Large Language Models (MLLMs) in video analysis. It features 900 videos totaling 254 hours with 2,700 human-annotated question-answer pairs across 6 primary visual domains (Knowledge, Film & Television, Sports Competition, Life Record, Multilingual, and others) and 30 subfields. The benchmark evaluates models across diverse temporal dimensions (11 seconds to 1 hour), integrates multi-modal inputs including video frames, subtitles, and audio, and uses rigorous manual labeling by expert annotators for precise assessment.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
17 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.