image to text benchmark
OCRBench
OCRBench: Comprehensive evaluation benchmark for assessing Optical Character Recognition (OCR) capabilities in Large Multimodal Models across text recognition, scene text VQA, and document understanding tasks
Updated Aug 11, 2026
Models22
Model coverage22
MetricScore
EvidenceB
OCRBench Ranking
Higher score ranks better on this benchmark.
OCRBench Score Distribution
A closer view of the leading scores on this benchmark.
OCRBench Highlights
The leading models and scores on this benchmark.
What is OCRBench?
What OCRBench measures and how its scores work.
OCRBench: Comprehensive evaluation benchmark for assessing Optical Character Recognition (OCR) capabilities in Large Multimodal Models across text recognition, scene text VQA, and document understanding tasks
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- OCRBench
- Modality
- multimodal
- Primary category
- image to text
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- ocrbench|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about OCRBench.
Which model scores highest on OCRBench?
Kimi K2.5 is currently ranked first with 92.3%.
What does OCRBench measure?
OCRBench: Comprehensive evaluation benchmark for assessing Optical Character Recognition (OCR) capabilities in Large Multimodal Models across text recognition, scene text VQA, and document understanding tasks
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
22 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.