Pyyan / Compare / Docling vs Qwen3-VL vs DeepSeek-OCR
Docling vs Qwen3-VL vs DeepSeek-OCR
3 of 5OCR & Document AI · verified 13 Aug 2026
| Specification | Docling | Qwen3-VL | DeepSeek-OCR |
|---|---|---|---|
| Summary | Document conversion aimed squarely at RAG pipelines. | A general vision model that happens to lead OCR benchmarks. | Compresses pages into far fewer vision tokens. |
| OmniDocBench | ~88 | ~93 | ~92 |
| Open weights | Yes | Yes | Yes |
| Handles | PDF, DOCX, PPTX, HTML | Documents, charts, video | Dense text, tables |
| Licence | MIT | Apache 2.0 | MIT |
| Kind | Pipeline | Vision language model | Vision language model |
| Category | OCR & Document AI | OCR & Document AI | OCR & Document AI |
| Official | IBM ↗ | Alibaba ↗ | DeepSeek ↗ |
Highlighted rows are where these differ.
Docling
- Outputs a structured document model, not just text
- Integrates with LlamaIndex and LangChain
Best for feeding a retrieval pipeline.
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.