BlogLLM Benchmark Scores as Published, Side by Side
VideoMMMU: Results as Published
Every result the catalog holds for VideoMMMU: 24 models from 6 developers, read from 8 pages. Each number is as printed by the company named beside it; nothing is ranked or rescored. Models are listed by developer, newest first.
Area Multimodal UnderstandingMaintainer LMMs-Lab (Nanyang Technological University)Introduced 2025Data version 2026103001, as of 2026-10-30
Video-MMMU tests whether a multimodal model can learn from videos. It holds 300 expert-level lecture videos across six disciplines with 900 questions arranged in three stages of knowledge acquisition: perception (reading what is shown), comprehension (understanding the concept) and adaptation (applying it to a new problem). Accuracy on the questions is the score.
Video-MMMU
| Model | Value | Printed by | Setup as printed |
|---|---|---|---|
| GPT-5.5OpenAI, released 2026-04-23 | 86.4% | MiniMax, MiniMax M3checked 2026-10-15 | - |
| GPT-5.2OpenAI, released 2025-12-11 | 85.9% | OpenAI, Introducing GPT-5.2checked 2026-10-15; printed on 2 more pages | Effort: xhigh; Tools: none |
| GPT-5.1OpenAI, released 2025-11-12 | 82.9% | OpenAI, Introducing GPT-5.2checked 2026-10-15 | Effort: high; Tools: none |
| GPT-5OpenAI, released 2025-08-07 | 84.6% | OpenAI, Introducing GPT-5checked 2026-10-15 | Effort: high |
| 61.6% | OpenAI, Introducing GPT-5checked 2026-10-15 | Effort: none | |
| GPT-5 miniOpenAI, released 2025-08-07 | 82.5% | Alibaba, Qwen3.5-122B-A10B model cardchecked 2026-10-15 | - |
| o3OpenAI, released 2025-04-16 | 83.3% | OpenAI, Introducing GPT-5checked 2026-10-15 | Effort: high |
| GPT-4oOpenAI, released 2024-05-13 | 61.2% | OpenAI, Introducing GPT-5checked 2026-10-15 | - |
| Claude Opus 4.7Anthropic, released 2026-04-16 | 83% | MiniMax, MiniMax M3checked 2026-10-15 | - |
| Claude Opus 4.5Anthropic, released 2025-11-24 | 84.4% | Alibaba, Qwen3.5-397B-A17B model cardchecked 2026-10-15; printed on 2 more pages | - |
| Claude Sonnet 4.5Anthropic, released 2025-09-29 | 77.6% | Alibaba, Qwen3.5-122B-A10B model cardchecked 2026-10-15; printed on 1 more page | - |
| Gemma 4 26B A4BGoogle, released 2026-04-02 | 76% | Alibaba, Qwen3.6-35B-A3B model cardchecked 2026-10-15 | - |
| Gemma 4 31BGoogle, released 2026-04-02 | 81.6% | Alibaba, Qwen3.6-27B model cardchecked 2026-10-15; printed on 1 more page | - |
| Gemini 3.1 Pro PreviewGoogle, released 2026-02-19 | 87.9% | MiniMax, MiniMax M3checked 2026-10-15 | - |
| Gemini 3 ProGoogle, released 2025-11-18 | 87.6% | Alibaba, Qwen3.5-397B-A17B model cardchecked 2026-10-15; printed on 1 more page | - |
| Qwen3.6-27BAlibaba, released 2026-04-22 | 84.4% | Alibaba, Qwen3.6-27B model cardchecked 2026-10-15 | - |
| Qwen3.6-35B-A3BAlibaba, released 2026-04-16 | 83.7% | Alibaba, Qwen3.6-35B-A3B model cardchecked 2026-10-15; printed on 1 more page | - |
| Qwen3.5-122B-A10BAlibaba, released 2026-02-24 | 82% | Alibaba, Qwen3.5-122B-A10B model cardchecked 2026-10-15 | - |
| Qwen3.5-27BAlibaba, released 2026-02-24 | 82.3% | Alibaba, Qwen3.5-122B-A10B model cardchecked 2026-10-15; printed on 2 more pages | - |
| Qwen3.5-35B-A3BAlibaba, released 2026-02-24 | 80.4% | Alibaba, Qwen3.5-122B-A10B model cardchecked 2026-10-15; printed on 1 more page | - |
| Qwen3.5-397B-A17BAlibaba, released 2026-02-16 | 84.7% | Alibaba, Qwen3.5-397B-A17B model cardchecked 2026-10-15; printed on 1 more page | - |
| Qwen3-VL-235B-A22BAlibaba, released 2025-09-23 | 80% | Alibaba, Qwen3.5-122B-A10B model cardchecked 2026-10-15; printed on 1 more page | - |
| Qwen3-VL-235B-A22B-ThinkingAlibaba, released 2025-09-23 | 80% | Moonshot AI, Kimi K2.5 model cardchecked 2026-10-15 | - |
| Kimi K2.5Moonshot AI, released 2026-01-27 | 86.6% | Moonshot AI, Kimi K2.5 model cardchecked 2026-10-15; printed on 1 more page | - |
| MiniMax M3MiniMax, released 2026-06-01 | 84.6% | MiniMax, MiniMax M3checked 2026-10-15 | - |
Every number here is read from the same catalog file as the benchmark table, data version 2026103001, and is the number the table shows for that model and test. Where one page printed a model under several setups, each value is listed. The catalog is public in armanobosyan/sugra-llm-benchmarks; a wrong number can be reported from the table, with Report an error under the value, or as an issue in that repository.