Pyyan / Compare / DeepSeek-OCR vs GLM-OCR vs dots.ocr vs Mistral OCR
DeepSeek-OCR vs GLM-OCR vs dots.ocr vs Mistral OCR
4 of 5OCR & Document AI · verified 13 Aug 2026
| Specification | DeepSeek-OCR | GLM-OCR | dots.ocr | Mistral OCR |
|---|---|---|---|---|
| Summary | Compresses pages into far fewer vision tokens. | Currently the top scorer on document parsing. | Small, multilingual, layout-aware. | A hosted API built for document ingestion. |
| OmniDocBench | ~92 | 94.6 | ~93 | ~92 |
| Open weights | Yes | Yes | Yes | No |
| Handles | Dense text, tables | Tables, formulas, handwriting | Layout, 100+ languages | Tables, images, equations |
| Licence | MIT | Open weights | MIT | Proprietary |
| Kind | Vision language model | Vision language model | Vision language model | Hosted API |
| Category | OCR & Document AI | OCR & Document AI | OCR & Document AI | OCR & Document AI |
| Official | DeepSeek ↗ | Zhipu AI ↗ | Xiaohongshu ↗ | Mistral AI ↗ |
Highlighted rows are where these differ.
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.
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.
dots.ocr
- Layout and content in one pass
- Strong for its size
Best for multilingual layout parsing.
Mistral OCR
- Priced per page
- Returns structured markdown
Best for teams who want no infrastructure.