Pyyan / Compare / GOT-OCR 2.0 vs dots.ocr vs Qwen3-VL vs DeepSeek-OCR
GOT-OCR 2.0 vs dots.ocr vs Qwen3-VL vs DeepSeek-OCR
4 of 5OCR & Document AI · verified 13 Aug 2026
| Specification | GOT-OCR 2.0 | dots.ocr | Qwen3-VL | DeepSeek-OCR |
|---|---|---|---|---|
| Summary | General OCR theory, one model for many document types. | Small, multilingual, layout-aware. | A general vision model that happens to lead OCR benchmarks. | Compresses pages into far fewer vision tokens. |
| OmniDocBench | ~90 | ~93 | ~93 | ~92 |
| Open weights | Yes | Yes | Yes | Yes |
| Handles | Formulas, music, charts | Layout, 100+ languages | Documents, charts, video | Dense text, tables |
| Licence | Apache 2.0 | MIT | Apache 2.0 | MIT |
| Kind | Vision language model | Vision language model | Vision language model | Vision language model |
| Category | OCR & Document AI | OCR & Document AI | OCR & Document AI | OCR & Document AI |
| Official | StepFun ↗ | Xiaohongshu ↗ | Alibaba ↗ | DeepSeek ↗ |
Highlighted rows are where these differ.
GOT-OCR 2.0
- Handles notation most OCR ignores
- Small enough to self-host easily
Best for formulas, sheet music, charts.
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.
DeepSeek-OCR
- Treats the page image as compressed context
- Notable for cost per page rather than raw accuracy
Best for long documents on a budget.