m-WildVision
Vision-language models answer WildVision bench prompts machine-translated into 23 languages. A model judge compares two models' answers and reports win rates.
Built from the English WildVision vision_bench_0617 test set with Google Translate. It is a translated copy, but labs report it as its own multilingual test.
- Level
- Everyday Most adults, without training
- Scored
- Graded by a model A language model scores each response, usually against a rubric.
- Answers
- Open-ended Free-form text, documents, designs or media judged as a whole.
Aya Vision 32B leads with 72%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 72% | Mar 2025 | Model card |
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
No launch post has listed it since Mar 2025
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| 4 Mar 2025 | 72 | Up to 72% win rate on m-WildVision (best pairwise win rate) |
One system card reports it
Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.
| Date | Document | Score | As the document writes it | Section |
|---|---|---|---|---|
| 13 May 2025 | | m-WildVision, pairwise win rates averaged over 23 languages, no overall: vs Llama-3.2-90B-Vision 73.0, vs Molmo-72B 68.8, vs Qwen-2.5-VL-72B 53.3 (Figure 7, printed labels) | Capabilities |
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