GOT-OCR 2.0 or PaddleOCR-VL: choosing an OCR model

Both models are small, both are Apache 2.0 with no commercial strings attached, and both run on an ordinary computer without a powerful GPU. GOT-OCR 2.0 from StepFun is 580M and reads text, formulas, tables, sheet music and diagrams, but its last release was 2024-11 and the catalog states plainly that it now lags behind newer models. PaddleOCR-VL from Baidu is 0.9B, was updated through 2026-05, and per its model card supports 109 languages including Russian, recognises tables, formulas and stamps, and outputs Markdown and JSON. For Russian invoices and contracts the choice is close to obvious.

Comparison based on catalog data

ParameterGOT-OCR 2.0PaddleOCR-VL
CategoryDocuments and OCRDocuments and OCR
DeveloperStepFun, ChinaBaidu (PaddlePaddle), China
ReleasesSep 2024 – Nov 2024Oct 2025 – May 2026
Sizes580M0.9B
HardwareLaptopLaptop
Commercial useCommercial use allowedCommercial use allowed
LicenseApache 2.0Apache 2.0
RussianNot statedSupported
OllamaNoNo
Without GPUYesYes
Tasks
  • Recognising scanned invoices and contracts
  • Converting tables into an editable format
  • Recognising formulas and diagrams
  • 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

Choose GOT-OCR 2.0 if

  • You need the smallest model: 580M against 0.9B
  • Your documents contain sheet music or unusual diagrams
  • It is a one-off job with no Russian-language requirement
GOT-OCR 2.0

Choose PaddleOCR-VL if

  • Your documents are in Russian: the catalog marks PaddleOCR-VL as supporting it
  • You need tables, formulas and stamps with Markdown or JSON output
  • You want something current: version 1.6 shipped in 2026-05
PaddleOCR-VL

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