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MLVU
Can the model extract and reason over information in images or documents?
Version / measure
- Domain
- Vision
- Ranked models
- 7
- Reported values
- 9
- Reports
- 2 · lab
- Direction
- Higher is better
- Status
- Version not specified
Rankings
Best reported score per model across effort settings
| # | Model | Best setting | Score |
|---|---|---|---|
| 1 | Qwen3.5-397B-A17BQwen | reportedcost n/a1 value · Apr 21 · source | 86.7% |
| 2 | Qwen3.6-27BQwen | reportedcost n/a1 value · Apr 21 · source | 86.6% |
| 3 | Qwen3.6-35B-A3BQwen | reportedcost n/a2 values · Apr 21 · source | 86.2% |
| 4 | Qwen3.5-27BQwen | reportedcost n/a2 values · Apr 21 · source | 85.9% |
| 5 | Qwen3.5-35B-A3BQwen | reportedcost n/a1 value · Apr 15 · source | 85.6% |
| 6 | Claude Opus 4.5Anthropic | reportedcost n/a1 value · Apr 21 · source | 81.7% |
| 7 | Claude Sonnet 4.5Anthropic | reportedcost n/a1 value · Apr 15 · source | 72.8% |
Effort curve
Score vs. reported cost. Lines connect complete sweeps only.
No cost-linked effort sweep for this version.
Definition and comparison boundarypublic methodology
Can the model extract and reason over information in images or documents?
Visual perception and multimodal reasoning under the reported protocol. Higher is better. The value is the percentage reported in this lab's table.
Harness, tools, prompts, attempt count, and benchmark version can materially change the result. Compare within one reporting context.
Method / source ↗