Deepseek OCR
UnclaimedNext-gen document intelligence with 97% accuracy across 100+ languages
What is Deepseek OCR?
DeepSeek OCR is a two-stage transformer-based document AI that compresses high-resolution pages into compact vision tokens, then decodes them with a 3B-parameter mixture-of-experts model. It delivers near-lossless text, layout, and diagram understanding across 100+ languages with 97% accuracy while processing 200k pages per day on a single GPU. Designed for organizations handling legal, financial, scientific, and multilingual documents at scale.
Key Features of Deepseek OCR
- Vision token compression (256 tokens per page)
- Multilingual support (100+ languages)
- Structured output (HTML, Markdown, SMILES)
- Layout-preserving OCR for tables and diagrams
- Mode selector (Tiny to Gundam for speed/fidelity tradeoffs)
- Mixture-of-experts decoder (~570M active parameters)
- FlashAttention GPU optimization
- MIT-licensed weights for on-premises deployment
- 200k pages/day throughput on NVIDIA A100
- Multimodal bridge for diagram and figure captions
Who Should Use Deepseek OCR?
Digitizing legal and financial PDFs at scale
Extracting structured data from invoices and forms
Processing scientific documents with chemistry formulas (SMILES strings)
Multilingual document conversion for global data generation projects
Automating table and diagram extraction for analytics pipelines
Handling large-format scans and blueprints
On-premises deployment for regulatory compliance
Deepseek OCR: Pros & Cons
✓Pros
- State-of-the-art 97% accuracy on structured documents
- Extremely high throughput (200k pages/day per GPU)
- Aggressive 10× compression while maintaining near-lossless fidelity
- Supports 100+ languages including specialized scientific scripts
- Structured outputs (HTML, Markdown, SMILES) integrate directly into analytics
- MIT-licensed for on-premises deployment without regulatory concerns
- Multimodal competence (captions, object grounding, diagram understanding)
- Flexible mode selector for speed/accuracy tradeoffs
- Trained on 30 million real PDF pages plus synthetic data
✕Cons
- Requires 8-10 GB GPU memory for base mode; 40 GB for Gundam mode
- API pricing at ~$0.028 per million input tokens may accumulate for high-volume use
- MIT license and open-source deployment require technical infrastructure
Tool Details
- Pricing
- Free
- Languages
- English, Simplified Chinese, Japanese, Korean, Traditional Chinese (Hong Kong), Traditional Chinese (Taiwan), and 94+ additional languages
- Category
- Document Tools
- Added
- Jun 2026
- Last Updated
- Jun 2026
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