Benchmark Almanac

m-WildVision

Aya Vision 32B 72% Dropped

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.

Made by Cohere For AI (Cohere Labs) Released Mar 2025 General visual questions All tasks public Last in a launch post Mar 2025 , dropped before it saturated Last model added May 2025
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.
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
1
System card reports it, from 1 lab

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.

  1. 1 Aya Vision 32B 72%
#ModelScoreSettingDateSource
1 Aya Vision 32B 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.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% 2025 Near the ceiling (85%) Aya Vision 32B 0% 20% 40% 60% 80% 100% 2025 Near the ceiling (85%) Aya Vision 32B

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.

Cohere
DateLaunch postScoreAs the post writes it
4 Mar 2025 Aya Vision 32B 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.

DateDocumentScoreAs the document writes itSection
13 May 2025 Aya Vision 32B technical report 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

More in Vision

All 234 in Vision
  • MMMU In use 11.5K college-level multimodal questions across 30 subjects. Multiple-choice and open accuracy. GPT-5.1 85.4%
  • MMMU-Pro In use Harder MMMU: college-level multimodal questions with more options and vision-only inputs to remove text shortcuts. Kimi K3.1 Pro 84.1%
  • CharXiv In use Chart understanding on real arXiv figures. Reasoning questions require synthesizing information from complex charts. Claude Fable 5 93.5%
  • MATH-Vision (MathVision) In use 3,040 competition math problems with visual context. Answer accuracy. Qwen 3.8 Max 97.7%
  • ChartQA In use Models answer human-written and generated questions about chart images that need visual and arithmetic reasoning. Scored by relaxed accuracy with a 5 percent numeric tolerance. Command A Vision 90.9%
  • DocVQA In use Models answer 50,000 questions about 12,000 scanned document images by reading their text and layout. Scoring is ANLS, a string similarity to reference answers. Seed1.5-VL 96.9%