Pyyan / Compare / olmOCR vs dots.ocr vs Qwen3-VL
olmOCR vs dots.ocr vs Qwen3-VL
3 of 5OCR & Document AI · verified 13 Aug 2026
| Specification | olmOCR | dots.ocr | Qwen3-VL |
|---|---|---|---|
| Summary | Fully open pipeline, weights and data. | Small, multilingual, layout-aware. | A general vision model that happens to lead OCR benchmarks. |
| OmniDocBench | ~91 | ~93 | ~93 |
| Open weights | Yes | Yes | Yes |
| Handles | Tables, markdown structure | Layout, 100+ languages | Documents, charts, video |
| Licence | Apache 2.0 | MIT | Apache 2.0 |
| Kind | Vision language model | Vision language model | Vision language model |
| Category | OCR & Document AI | OCR & Document AI | OCR & Document AI |
| Official | Allen Institute for AI ↗ | Xiaohongshu ↗ | Alibaba ↗ |
Highlighted rows are where these differ.
olmOCR
- Preserves reading order and markdown structure
- Training data published, which is rare
Best for reproducible research pipelines.
dots.ocr
- Layout and content in one pass
- Strong for its size
Best for multilingual layout parsing.
Qwen3-VL
- Not an OCR model, and beats most of them
- Sizes from 2B to 235B
Best for one model for vision and documents.