image to text benchmark
DocVQA
A dataset for Visual Question Answering on document images containing 50,000 questions defined on 12,000+ document images. The benchmark tests AI's ability to understand document structure and content, requiring models to comprehend document layout and perform information retrieval to answer questions about document images.
Updated Aug 11, 2026
Models26
Model coverage26
MetricScore
EvidenceB
DocVQA Ranking
Higher score ranks better on this benchmark.
DocVQA Score Distribution
A closer view of the leading scores on this benchmark.
DocVQA Highlights
The leading models and scores on this benchmark.
What is DocVQA?
What DocVQA measures and how its scores work.
A dataset for Visual Question Answering on document images containing 50,000 questions defined on 12,000+ document images. The benchmark tests AI's ability to understand document structure and content, requiring models to comprehend document layout and perform information retrieval to answer questions about document images.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- DocVQA
- Modality
- multimodal
- Primary category
- image to text
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- docvqa|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about DocVQA.
Which model scores highest on DocVQA?
Qwen2.5 VL 72B Instruct is currently ranked first with 96.4%.
What does DocVQA measure?
A dataset for Visual Question Answering on document images containing 50,000 questions defined on 12,000+ document images. The benchmark tests AI's ability to understand document structure and content, requiring models to comprehend document layout and perform information retrieval to answer questions about document images.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
26 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.