image to text benchmark
OCRBench v2 English subset: Enhanced benchmark for evaluating Large Multimodal Models on visual text localization and reasoning with English text content
Updated Aug 7, 2026
Sorted by the source-provided rank. Higher score is better according to the registry.
| 1 | AC | 68.4% | 100.0% | 12 | C | |
| 2 | AC | 67.4% | 90.9% | 12 | C | |
| 3 | AC | 67.1% | 81.8% | 12 | C | |
| 4 | AC | 66.8% | 72.7% | 12 | C | |
| 5 | AC | 65.4% | 63.6% | 12 | C | |
| 6 | AC | 63.9% | 54.5% | 12 | C | |
| 7 | AC | 63.7% | 45.5% | 12 | C | |
| 8 | AC | 63.2% | 36.4% | 12 | C | |
| 9 | AC | 62.6% | 27.3% | 12 | C | |
| 10 | AC | 61.8% | 18.2% | 12 | C | |
| 11 | AC | 61.5% | 9.1% | 12 | C | |
| 12 | AC | 57.2% | 0.0% | 12 | C |
Top published rows on the benchmark's original scale.
The top published results on this benchmark's own scale.
Definition and scoring fields from the benchmark registry.
OCRBench v2 English subset: Enhanced benchmark for evaluating Large Multimodal Models on visual text localization and reasoning with English text content
Scores are shown in ratio. The current registry marks this benchmark as not independently verified with evidence level B.
Source-native results are preserved. Eligibility for the overall LLMBoard score is a separate policy decision.
Common questions about OCRBench-V2 (en).
Qwen3 VL 32B Thinking is currently ranked first with 68.4%.
OCRBench v2 English subset: Enhanced benchmark for evaluating Large Multimodal Models on visual text localization and reasoning with English text content
Yes. Higher values rank better for this benchmark.
12 unique published model results are currently shown.
This benchmark is preserved as source-native evidence but is not eligible for the current overall score.