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 7, 2026
Published models22
Registry coverage22
MetricScore
EvidenceB
OCRBench leaderboard
Sorted by the source-provided rank. Higher score is better according to the registry.
Score distribution
Top published rows on the benchmark's original scale.
OCRBench highlights
The top published results on this benchmark's own scale.
What is OCRBench?
Definition and scoring fields from the benchmark registry.
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. The current registry marks this benchmark as not independently verified with evidence level B.
- Family
- OCRBench
- Modality
- multimodal
- Primary category
- image to text
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- ocrbench|llm-stats-current
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
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 unique published model results are currently shown.
Does this benchmark affect the overall score?
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.