PyyanThe AI Index

Pyyan / Compare / Qwen3-VL vs dots.ocr vs DeepSeek-OCR vs Mistral OCR

Qwen3-VL vs dots.ocr vs DeepSeek-OCR vs Mistral OCR

4 of 5

OCR & Document AI · verified 13 Aug 2026

×Qwen3-VLAlibabacurrent
×dots.ocrXiaohongshucurrent
×DeepSeek-OCRDeepSeekcurrent
×Mistral OCRMistral AIcurrent
1 slot left
SpecificationQwen3-VLdots.ocrDeepSeek-OCRMistral OCR
SummaryA general vision model that happens to lead OCR benchmarks.Small, multilingual, layout-aware.Compresses pages into far fewer vision tokens.A hosted API built for document ingestion.
OmniDocBench~93~93~92~92
Open weightsYesYesYesNo
HandlesDocuments, charts, videoLayout, 100+ languagesDense text, tablesTables, images, equations
LicenceApache 2.0MITMITProprietary
KindVision language modelVision language modelVision language modelHosted API
CategoryOCR & Document AIOCR & Document AIOCR & Document AIOCR & Document AI
OfficialAlibaba ↗Xiaohongshu ↗DeepSeek ↗Mistral AI ↗

Highlighted rows are where these differ.

Qwen3-VL

  • Not an OCR model, and beats most of them
  • Sizes from 2B to 235B

Best for one model for vision and documents.

Full spec sheet →

dots.ocr

  • Layout and content in one pass
  • Strong for its size

Best for multilingual layout parsing.

Full spec sheet →

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.

Full spec sheet →

Mistral OCR

  • Priced per page
  • Returns structured markdown

Best for teams who want no infrastructure.

Full spec sheet →