multimodal benchmark
VideoMME w/o sub. Leaderboard
Video-MME is a comprehensive evaluation benchmark for multi-modal large language models in video analysis. It features 900 videos across 6 primary visual domains with 30 subfields, ranging from 11 seconds to 1 hour in duration, with 2,700 question-answer pairs. The benchmark evaluates MLLMs' capabilities in processing sequential visual data and multi-modal content including video frames, subtitles, and audio.
Updated Aug 17, 2026
Models10
Model coverage10
MetricScore
EvidenceB
VideoMME w/o sub. Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score83.9% | Percentile100.0% | Participants10 | EvidenceC | Evaluated |
| Rank02 | ModelAC | Score82.8% | Percentile88.9% | Participants10 | EvidenceC | Evaluated |
| Rank03 | ModelAC | Score82.5% | Percentile77.8% | Participants10 | EvidenceC | Evaluated |
| Rank04 | ModelAC | Score82.5% | Percentile66.7% | Participants10 | EvidenceC | Evaluated |
| Rank05 | ModelAC | Score79.2% | Percentile55.6% | Participants10 | EvidenceC | Evaluated |
| Rank06 | ModelAC | Score79.0% | Percentile44.4% | Participants10 | EvidenceC | Evaluated |
| Rank07 | ModelAC | Score77.3% | Percentile33.3% | Participants10 | EvidenceC | Evaluated |
| Rank08 | ModelAC | Score73.3% | Percentile22.2% | Participants10 | EvidenceC | Evaluated |
| Rank09 | ModelAC | Score70.5% | Percentile11.1% | Participants10 | EvidenceC | Evaluated |
| Rank10 | ModelAC | Score65.1% | Percentile0.0% | Participants10 | EvidenceC | Evaluated |
VideoMME w/o sub. Highlights
The leading models and scores on this benchmark.
VideoMME w/o sub. Score Distribution
A closer view of the leading scores on this benchmark.
The Top AI Models for VideoMME w/o sub.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis videomme w/o sub. AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01ACQwen3.5-122B-A10BAlibaba Cloud / Qwen Team
- Score
- 83.9%
- Price
- $0.40 input / $3.2 output per 1M tokens
Strengths
- Ranks #1 of 10 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures VideoMME w/o sub., not total model capability
- 02ACQwen3.5-27BAlibaba Cloud / Qwen Team
- Score
- 82.8%
- Price
- $0.30 input / $2.4 output per 1M tokens
- Speed
- Up to 6.7 tok/s via Novita
Strengths
- Ranks #2 of 10 compared models
- 89th percentile on this benchmark
- C evidence result
Considerations
- This result measures VideoMME w/o sub., not total model capability
- 03ACQwen3.5-35B-A3BAlibaba Cloud / Qwen Team
- Score
- 82.5%
- Price
- $0.25 input / $2.0 output per 1M tokens
Strengths
- Ranks #3 of 10 compared models
- 78th percentile on this benchmark
- C evidence result
Considerations
- This result measures VideoMME w/o sub., not total model capability
- 04ACQwen3.6-35B-A3BAlibaba Cloud / Qwen Team
- Score
- 82.5%
- Price
- $0.25 input / $1.5 output per 1M tokens
Strengths
- Ranks #4 of 10 compared models
- 67th percentile on this benchmark
- C evidence result
Considerations
- This result measures VideoMME w/o sub., not total model capability
- 05ACQwen3 VL 235B A22B InstructAlibaba Cloud / Qwen Team
- Score
- 79.2%
Strengths
- Ranks #5 of 10 compared models
- 56th percentile on this benchmark
- C evidence result
Considerations
- This result measures VideoMME w/o sub., not total model capability
Selection summary
Best AI Models for VideoMME w/o sub.
Qwen3.5-122B-A10B currently leads VideoMME w/o sub. with 83.9%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What is VideoMME w/o sub.?
What VideoMME w/o sub. measures and how its scores work.
Video-MME is a comprehensive evaluation benchmark for multi-modal large language models in video analysis. It features 900 videos across 6 primary visual domains with 30 subfields, ranging from 11 seconds to 1 hour in duration, with 2,700 question-answer pairs. The benchmark evaluates MLLMs' capabilities in processing sequential visual data and multi-modal content including video frames, subtitles, and audio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- VideoMME w/o sub.
- Modality
- multimodal
- Primary category
- multimodal
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- videomme-w-o-sub.|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about VideoMME w/o sub..
Which model scores highest on VideoMME w/o sub.?
Qwen3.5-122B-A10B is currently ranked first with 83.9%.
What does VideoMME w/o sub. measure?
Video-MME is a comprehensive evaluation benchmark for multi-modal large language models in video analysis. It features 900 videos across 6 primary visual domains with 30 subfields, ranging from 11 seconds to 1 hour in duration, with 2,700 question-answer pairs. The benchmark evaluates MLLMs' capabilities in processing sequential visual data and multi-modal content including video frames, subtitles, and audio.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
10 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.