Document AI developers
Add OCR and layout recovery to a controlled application or processing pipeline.
Independent tool overview
DeepSeek OCR 2 at a glance
DeepSeek-OCR 2 is an open-weight 3B vision-language model for extracting text and document structure from images and PDFs.
Visit the official DeepSeek OCR 2 site ↗
- Model size
- 3B parameters
- License
- Apache 2.0
- Primary outputs
- Plain OCR or layout-aware Markdown
- Reference runtimes
- Transformers and vLLM
- Last reviewed
- August 30, 2026
Overview
What DeepSeek OCR 2 is
DeepSeek-OCR 2 is a developer-focused optical character recognition model that converts document images into text or layout-aware Markdown. DeepSeek publishes the model weights, reference code, paper, and examples for local or self-managed deployment.
Its central research idea is Visual Causal Flow. DeepEncoder V2 dynamically reorders visual tokens according to image semantics instead of processing every page in a fixed top-left-to-bottom-right raster order. The approach is intended to better follow the logical reading order of complex page layouts.
This is not a hosted drag-and-drop document service. Teams need a compatible Python and GPU environment, must build ingestion and output handling, and should benchmark the model on their own languages, scans, forms, tables, equations, handwriting, and layout types before production use.
Use cases
Who DeepSeek OCR 2 is best for
The strongest fit depends on the job you need the product to complete, not the size of its feature list.
Self-hosted workflows
Run open weights in infrastructure the team manages rather than sending documents to a bundled SaaS interface.
PDF-to-Markdown pipelines
Extract readable text while preserving useful page structure for search, retrieval, or downstream language models.
OCR research and evaluation
Study semantic visual-token ordering or compare document-understanding models on an internal benchmark.
Capabilities
Core DeepSeek OCR 2 features
Layout-aware Markdown extraction
The official grounded prompt converts a document image into Markdown intended to retain meaningful structure.
Plain OCR mode
A separate Free OCR prompt extracts text without emphasizing document layout.
Visual Causal Flow
DeepEncoder V2 uses causal reasoning to reorder visual tokens based on page semantics before language-model interpretation.
Dynamic resolution
The reference implementation supports variable image crops and visual-token counts for different page complexity.
Image and PDF examples
The repository includes scripts for single-image output, concurrent PDF processing, and benchmark evaluation.
Open deployment paths
DeepSeek documents Transformers and vLLM inference, and publishes the model as safetensors on Hugging Face.
Process
How the DeepSeek OCR 2 workflow works
Step 1
Define the target documents
Collect representative clean pages, poor scans, tables, formulas, multi-column layouts, languages, and edge cases with approved ground truth.
Step 2
Prepare the environment
Install the repository's pinned dependencies and configure compatible NVIDIA GPU, CUDA, PyTorch, FlashAttention, and model-storage resources.
Step 3
Choose the extraction prompt
Use grounded Markdown conversion when layout matters or the plain OCR prompt when only reading text is required.
Step 4
Run and normalize output
Process images or PDFs, retain page boundaries, and normalize Markdown, coordinates, tables, and metadata for the downstream system.
Step 5
Evaluate before production
Measure text accuracy, reading order, table and formula quality, latency, GPU memory, failure rate, and human-review load on internal documents.
Cost
DeepSeek OCR 2 pricing and free plan
DeepSeek publishes the DeepSeek-OCR 2 code and model under the Apache 2.0 license. There is no DeepSeek subscription price for downloading the open model, but deployment is not cost-free: teams pay for GPU compute, storage, engineering, monitoring, security, and any third-party hosting they choose.
Open model
Free to download
DeepSeek provides the 3B model weights and code for self-managed use under Apache 2.0.
- Apache 2.0 license
- Self-hosting infrastructure not included
- Operational and engineering costs vary
Pricing checked . Check current pricing at the source ↗
Assessment
DeepSeek OCR 2 strengths and limitations
Where it stands out
- Open weights and reference code give teams control over deployment and document handling.
- Supports both plain text extraction and layout-aware Markdown output.
- The 3B parameter size is relatively compact for a document-focused vision-language model.
- Official examples cover image, PDF, Transformers, vLLM, and benchmark workflows.
What to consider
- The official setup is technical and tested with a specific CUDA, PyTorch, Python, vLLM, and FlashAttention stack.
- Self-hosting requires GPU capacity plus application code for uploads, queues, retries, parsing, security, and monitoring.
- OCR quality can vary materially across languages, handwriting, low-resolution scans, tables, equations, forms, and unusual layouts.
- Loading model-defined custom code requires the same dependency and supply-chain review applied to other production software.
- Published benchmark performance does not replace evaluation on the documents and error costs of the intended workflow.
Compare
DeepSeek OCR 2 alternatives
The right alternative depends on the specific output, workflow, controls and budget your project requires.
Business Operations
GLM-OCR
An alternative OCR-focused multimodal model to compare for text, formula, table, and structured-document extraction.
Explore GLM-OCR →Business Operations
HunyuanOCR
Consider Tencent's open visual-understanding model when evaluating multilingual OCR and self-hosted options.
Explore HunyuanOCR →Business Operations
Mistral OCR 3
A strong comparison for teams that prefer a managed document-understanding API rather than operating an open model themselves.
Explore Mistral OCR 3 →Questions
DeepSeek OCR 2 FAQs
What is DeepSeek-OCR 2?
DeepSeek-OCR 2 is a 3B parameter vision-language model and research release for extracting text and document structure from images and PDFs.
What is Visual Causal Flow?
It is DeepSeek's approach to reordering visual tokens according to the semantic and logical structure of an image before language-model interpretation, rather than relying only on fixed raster-scan order.
Is DeepSeek-OCR 2 free?
The official code and model are available under the Apache 2.0 license. Compute, storage, engineering, security, and any hosting provider remain separate costs.
Can it convert a PDF to Markdown?
Yes. The repository includes PDF processing examples, and its grounded document prompt is designed to convert page content into layout-aware Markdown.
Does DeepSeek provide a hosted OCR interface?
The official release is centered on model weights, code, and local or self-managed inference. A production web interface, document pipeline, review process, and service guarantees are not included.
Should I trust the output without review?
No. Validate extracted text and structure on representative documents, and require human review where errors could affect money, compliance, safety, legal obligations, or customer records.
Bottom line
Our DeepSeek OCR 2 verdict
DeepSeek-OCR 2 is a notable open option for engineering teams that want layout-aware document extraction and control over deployment. Its compact model and published reference workflows are attractive, but the real decision should come from an internal benchmark that includes operating cost, reading-order quality, difficult layouts, and the cost of correcting errors.
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