DeepSeek-OCR vs Unlimited OCR
Which should you use? A side-by-side look at what each one does, who it suits, what it costs, how hard it is to set up, and where each falls short.
Open source · MIT
An OCR model that turns document images into text and markdown, from DeepSeek AI.
Open source · MIT
Open-source OCR model from Baidu that parses long, multi-page documents into text.
The short answer
Choose DeepSeek-OCR if you need…
- Developers who want an open-weight OCR model they can run themselves
- Teams converting scanned documents into markdown or plain text
- Researchers studying visual-text compression with vision encoders
- Users who already have a CUDA GPU environment set up
Choose Unlimited OCR if you need…
- Developers who want to parse multi-page scanned documents with a locally hosted model
- Teams that need to process long PDFs converted to page images
- Researchers comparing long-document OCR models using the MarathonDocBench benchmark
- Engineers who already run GPU-based model serving with SGLang
Side-by-side
| DeepSeek-OCR | Unlimited OCR | |
|---|---|---|
| Type | AI model / agent | AI model / agent |
| Price | Free and open source | Free and open source |
| Licence | MIT | MIT |
| GitHub stars | 24.1k | 30.3k |
| Setup difficulty | Advanced | Advanced |
| How you get it | Clone and build from source | Install with a package manager |
| Main language | Python | Python |
| Last code update | 9 months ago | 4 days ago |
What each one is
DeepSeek-OCR
DeepSeek-OCR is a research model released by DeepSeek AI that reads text from images, such as scanned pages or PDFs, and converts them into text or markdown. The README frames it as an investigation into compressing text into visual tokens, using vision encoders from an LLM-centric viewpoint. It is mainly used by developers who want to run OCR locally through Python.
Unlimited OCR
Unlimited OCR is a document-parsing model from Baidu that reads images of pages and converts them into structured text. The README says its second version, Unlimited OCR 2, is designed to handle very long documents, including multi-page and hundred-page files, by using a sparse attention method called SR-SWA. It is released under the MIT license and can be run through Hugging Face Transformers or SGLang.
Best for
DeepSeek-OCR
- Developers who want an open-weight OCR model they can run themselves
- Teams converting scanned documents into markdown or plain text
- Researchers studying visual-text compression with vision encoders
- Users who already have a CUDA GPU environment set up
Unlimited OCR
- Developers who want to parse multi-page scanned documents with a locally hosted model
- Teams that need to process long PDFs converted to page images
- Researchers comparing long-document OCR models using the MarathonDocBench benchmark
- Engineers who already run GPU-based model serving with SGLang
Strengths
DeepSeek-OCR
- Open weights and MIT-licensed code, with a Hugging Face model download
- Supports both vLLM and Transformers inference paths in the README
- Offers several resolution modes (Tiny to Large, plus Gundam) for different speed and detail trade-offs
Unlimited OCR
- Built to handle long documents in one pass, according to the README
- Released under the MIT license with public model weights on Hugging Face
- Offers both a simple Transformers path for quick tests and an SGLang path for serving
Not ideal for
DeepSeek-OCR
- People who need a point-and-click app: the README only shows Python scripts and commands
- Users without an NVIDIA/CUDA GPU setup, since the install instructions assume CUDA 11.8 and torch 2.6.0
- Teams needing guaranteed accuracy on specific document types, since the README shows examples but no accuracy benchmarks in the truncated text
Unlimited OCR
- People who need a simple point-and-click tool, since the README only shows Python and server commands
- Users without an NVIDIA GPU, because the README's examples and tested setup use CUDA hardware (A800-80GB)
- Anyone needing guaranteed accuracy on their own documents, since the README's benchmark claims are not independently verified here
Trade-offs
DeepSeek-OCR
- Setup is involved: it requires a specific CUDA/torch version, a vLLM wheel, and flash-attn
- The README is truncated and does not state hardware requirements beyond an A100-40G throughput note
Unlimited OCR
- Setup requires a GPU and specific pinned package versions, which can be fiddly
- Performance claims come from the authors' own benchmarks and leaderboard images
- Documentation is partly in code comments and the README is truncated, so some settings are unexplained
Getting started
DeepSeek-OCR
Clone the repository
git clone https://github.com/deepseek-ai/DeepSeek-OCR.gitCreate and activate the conda environment
conda create -n deepseek-ocr python=3.12.9 -y && conda activate deepseek-ocrUnlimited OCR
Install Transformers dependencies
pip install torch==2.13.0 torchvision pillow transformers==4.52.1 huggingface-hub==0.36.0 tokenizers==0.21.4 addict==2.4.0 einops==0.8.2 easydict==1.13Create SGLang virtualenv
uv venv --python 3.10Frequently asked questions
What is the difference between DeepSeek-OCR and Unlimited OCR?
DeepSeek-OCR: An OCR model that turns document images into text and markdown, from DeepSeek AI. Unlimited OCR: Open-source OCR model from Baidu that parses long, multi-page documents into text. DeepSeek-OCR suits developers who want an open-weight ocr model they can run themselves, while Unlimited OCR suits developers who want to parse multi-page scanned documents with a locally hosted model.
Are DeepSeek-OCR and Unlimited OCR free?
DeepSeek-OCR: Free and open source. Unlimited OCR: Free and open source.
Is DeepSeek-OCR or Unlimited OCR more popular?
Unlimited OCR has more GitHub stars (30.3k) than DeepSeek-OCR (24.1k). Popularity isn't the same as fit — check which one matches your use case above.
Built from our catalogue entries for both products: open-source details come from each project's README (AI-assisted, refreshed weekly) and GitHub data; AI-tool details are editorial. Spotted something wrong? Let us know from either product's page.