PaddleOCR-VL or DeepSeek-OCR: which to choose for business

Both allow commercial use and convert scans to Markdown. PaddleOCR-VL is smaller (0.9B), Apache 2.0, supports 109 languages including Russian per its card, recognizes tables, formulas and stamps, and runs on a regular PC without a powerful GPU; the catalog notes that version 1.6 leads the OmniDocBench test. DeepSeek-OCR (about 3B, MIT and Apache 2.0) compresses a page into a small number of visual tokens and targets bulk processing, and it is in Ollama; Russian support is not confirmed in its card and CPU use is not marked. PaddleOCR-VL was updated more recently (2026-05 vs 2026-01).

Comparison based on catalog data

ParameterPaddleOCR-VLDeepSeek-OCR
CategoryDocuments and OCRDocuments and OCR
DeveloperBaidu (PaddlePaddle), ChinaDeepSeek, China
ReleasesOct 2025 – May 2026Oct 2025 – Jan 2026
Sizes0.9Babout 3B
HardwareLaptopLaptop, 1 GPU
Commercial useCommercial use allowedCommercial use allowed
LicenseApache 2.0DeepSeek-OCR: MIT; DeepSeek-OCR 2: Apache 2.0
RussianSupportedNot stated
OllamaNoYes
Without GPUYesNo
Tasks
  • Recognising invoices, contracts and delivery notes, including in Russian
  • Recognising tables, formulas and stamps
  • Converting scans to Markdown and JSON
  • Running on an ordinary PC without a powerful GPU
  • Bulk recognition of scanned invoices and contracts
  • Table recognition
  • Converting PDFs to Markdown for search and RAG

Choose PaddleOCR-VL if

  • Your documents are in Russian: invoices, contracts, waybills
  • You need to recognize stamps, formulas and tables
  • You have no GPU: the model runs on a regular PC
PaddleOCR-VL

Choose DeepSeek-OCR if

  • You process a large archive of scans in bulk
  • You convert PDFs to Markdown for search and RAG
  • You want to run it through Ollama
DeepSeek-OCR

Other comparisons

Need a model for your task?

An open model can run on your own server: data stays in-house, there is no per-request fee, and the model can be fine-tuned on your documents.

  1. SelectThe model and size for your task and hardware budget
  2. DeployOn your server or in a closed network, with an API
  3. Fine-tuneOn your data, or connect a knowledge base
  4. IntegrateInto your CRM, ERP, bot, website or team chat
Discuss deployment