image to text benchmark
SlakeVQA
A semantically-labeled knowledge-enhanced dataset for medical visual question answering. Contains 642 radiology images (CT scans, MRI scans, X-rays) covering five body parts and 14,028 bilingual English-Chinese question-answer pairs annotated by experienced physicians. Features comprehensive semantic labels and a structural medical knowledge base with both vision-only and knowledge-based questions requiring external medical knowledge reasoning.
Updated Aug 11, 2026
Models4
Model coverage4
MetricScore
EvidenceB
SlakeVQA Ranking
Higher score ranks better on this benchmark.
| 01 | AC | 81.6% | 100.0% | 4 | C | |
| 02 | AC | 80.0% | 66.7% | 4 | C | |
| 03 | AC | 78.7% | 33.3% | 4 | C | |
| 04 | GO | 62.3% | 0.0% | 4 | C |
SlakeVQA Score Distribution
A closer view of the leading scores on this benchmark.
SlakeVQA Highlights
The leading models and scores on this benchmark.
What is SlakeVQA?
What SlakeVQA measures and how its scores work.
A semantically-labeled knowledge-enhanced dataset for medical visual question answering. Contains 642 radiology images (CT scans, MRI scans, X-rays) covering five body parts and 14,028 bilingual English-Chinese question-answer pairs annotated by experienced physicians. Features comprehensive semantic labels and a structural medical knowledge base with both vision-only and knowledge-based questions requiring external medical knowledge reasoning.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- SlakeVQA
- Modality
- multimodal
- Primary category
- image to text
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- slakevqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about SlakeVQA.
Which model scores highest on SlakeVQA?
Qwen3.5-122B-A10B is currently ranked first with 81.6%.
What does SlakeVQA measure?
A semantically-labeled knowledge-enhanced dataset for medical visual question answering. Contains 642 radiology images (CT scans, MRI scans, X-rays) covering five body parts and 14,028 bilingual English-Chinese question-answer pairs annotated by experienced physicians. Features comprehensive semantic labels and a structural medical knowledge base with both vision-only and knowledge-based questions requiring external medical knowledge reasoning.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
4 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.