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

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

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OCR & Document AI · verified 13 Aug 2026

×dots.ocrXiaohongshucurrent
×Qwen3-VLAlibabacurrent
×DeepSeek-OCRDeepSeekcurrent
×Mistral OCRMistral AIcurrent
1 slot left
Specificationdots.ocrQwen3-VLDeepSeek-OCRMistral OCR
SummarySmall, multilingual, layout-aware.A general vision model that happens to lead OCR benchmarks.Compresses pages into far fewer vision tokens.A hosted API built for document ingestion.
OmniDocBench~93~93~92~92
Open weightsYesYesYesNo
HandlesLayout, 100+ languagesDocuments, charts, videoDense text, tablesTables, images, equations
LicenceMITApache 2.0MITProprietary
KindVision language modelVision language modelVision language modelHosted API
CategoryOCR & Document AIOCR & Document AIOCR & Document AIOCR & Document AI
OfficialXiaohongshu ↗Alibaba ↗DeepSeek ↗Mistral AI ↗

Highlighted rows are where these differ.

dots.ocr

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

Best for multilingual layout parsing.

Full spec sheet →

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 →

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 →