OCRBench-V2 (en)
OCRBench v2 English subset: Enhanced benchmark for evaluating Large Multimodal Models on visual text localization and reasoning with English text content
Qwen3 VL 32B Thinking from Alibaba Cloud / Qwen Team currently leads the OCRBench-V2 (en) leaderboard with a score of 0.684 across 12 evaluated AI models.
What OCRBench-V2 (en) measures
OCRBench-V2 (en) is a multimodal benchmark that evaluates large language models on image to text and vision tasks. LLM Stats tracks 12 models on this benchmark, with a maximum possible score of 1. Current average across reported models is 0.6, with the leader reaching 0.7.
Compare leaders on the best AI for image to text and best AI for vision leaderboards.
Publication
- Paper
- OCRBench v2: An Improved Benchmark for Evaluating Large Multimodal Models on Visual Text Localization and Reasoning
- Authors
- Ling Fu, Zhebin Kuang, Jiajun Song, Mingxin Huang, and 20 others
- Published
- arXiv
- 2501.00321
Abstract
Scoring the Optical Character Recognition (OCR) capabilities of Large Multimodal Models (LMMs) has witnessed growing interest. Existing benchmarks have highlighted the impressive performance of LMMs in text recognition; however, their abilities in certain challenging tasks, such as text localization, handwritten content extraction, and logical reasoning, remain underexplored. To bridge this gap, we introduce OCRBench v2, a large-scale bilingual text-centric benchmark with currently the most comprehensive set of tasks (4x more tasks than the previous multi-scene benchmark OCRBench), the widest coverage of scenarios (31 diverse scenarios), and thorough evaluation metrics, with 10,000 human-verified question-answering pairs and a high proportion of difficult samples. Moreover, we construct a private test set with 1,500 manually annotated images. The consistent evaluation trends observed across both public and private test sets validate the OCRBench v2's reliability. After carefully benchmarking state-of-the-art LMMs, we find that most LMMs score below 50 (100 in total) and suffer from five-type limitations, including less frequently encountered text recognition, fine-grained perception, layout perception, complex element parsing, and logical reasoning. The project website is at: https://99franklin.github.io/ocrbench_v2/
Qwen3 VL 32B Thinking leads with 68.4%, followed by Qwen3 VL 32B Instruct at 67.4% and Qwen3 VL 235B A22B Instruct at 67.1%.
Progress Over Time
Interactive timeline showing model performance evolution on OCRBench-V2 (en)
OCRBench-V2 (en) Leaderboard
| Context | Cost | License | ||||
|---|---|---|---|---|---|---|
| 1 |  Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 2 | Alibaba Cloud / Qwen Team | 33B | — | — | ||
| 3 | Alibaba Cloud / Qwen Team | 236B | 262K | $0.30 / $1.50 | ||
| 4 | Alibaba Cloud / Qwen Team | 236B | — | — | ||
| 5 | Alibaba Cloud / Qwen Team | 9B | 262K | $0.08 / $0.50 | ||
| 6 | Alibaba Cloud / Qwen Team | 9B | 262K | $0.18 / $2.09 | ||
| 7 | Alibaba Cloud / Qwen Team | 4B | 262K | $0.10 / $0.60 | ||
| 8 | Alibaba Cloud / Qwen Team | 31B | — | — | ||
| 9 | Alibaba Cloud / Qwen Team | 31B | — | — | ||
| 10 | Alibaba Cloud / Qwen Team | 4B | 262K | $0.10 / $1.00 | ||
| 11 | Alibaba Cloud / Qwen Team | 72B | — | — | ||
| 12 | Alibaba Cloud / Qwen Team | 34B | — | — |
FAQ
Common questions about OCRBench-V2 (en).
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