Pyyan / Compare / dots.ocr vs GLM-OCR vs Qwen3-VL vs DeepSeek-OCR
dots.ocr vs GLM-OCR vs Qwen3-VL vs DeepSeek-OCR
4 of 5OCR & Document AI · verified 13 Aug 2026
| Specification | dots.ocr | GLM-OCR | Qwen3-VL | DeepSeek-OCR |
|---|---|---|---|---|
| Summary | Small, multilingual, layout-aware. | Currently the top scorer on document parsing. | A general vision model that happens to lead OCR benchmarks. | Compresses pages into far fewer vision tokens. |
| OmniDocBench | ~93 | 94.6 | ~93 | ~92 |
| Open weights | Yes | Yes | Yes | Yes |
| Handles | Layout, 100+ languages | Tables, formulas, handwriting | Documents, charts, video | Dense text, tables |
| Licence | MIT | Open weights | 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 | Xiaohongshu ↗ | Zhipu AI ↗ | Alibaba ↗ | DeepSeek ↗ |
Highlighted rows are where these differ.
dots.ocr
- Layout and content in one pass
- Strong for its size
Best for multilingual layout parsing.
GLM-OCR
- Ahead of Gemini 3 Pro and GPT-5.2 on the same benchmark
- 94.0 on OCRBench
Best for complex documents end to end.
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.