Deepseek Ocr
Next-gen document intelligence with context optical compression and multilingual support.
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DeepSeek OCR is a two-stage transformer-based document AI system that utilizes context optical compression to deliver state-of-the-art document intelligence. It compresses high-resolution documents into lean vision tokens, then decodes them with a 3B-parameter mixture-of-experts model to achieve near-lossless text, layout, and diagram understanding across 100+ languages. It supports GPU-efficient throughput for complex layouts and is trained on 30 million real PDF pages plus synthetic data, preserving layout structure, tables, chemistry (SMILES strings), and geometry tasks.
DeepSeek OCR can be used in three main ways: 1. Deploy locally with GPUs by cloning the GitHub repo, downloading the 6.7 GB checkpoint, and configuring PyTorch. 2. Call DeepSeek OCR via its OpenAI-compatible API endpoints to submit images and receive structured text. 3. Integrate DeepSeek OCR into existing workflows by converting OCR outputs to JSON, linking SMILES strings to cheminformatics pipelines, or auto-captioning diagrams.
You should choose this if you want a next-gen document AI that handles complex layouts and multiple languages with high precision. Deepseek OCR’s transformer-based tech and efficient processing make it a solid choice for serious document intelligence needs.
Per 1M input tokens when cache is hit
Per 1M input tokens when cache is missed
Per 1M output tokens
