What is DeepSeek-OCR 2?
DeepSeek-OCR 2 is an open-weight vision-language model from DeepSeek. In practical terms, it looks at an image of a document and produces text based on a prompt. Its intended tasks include reading scanned pages, transcribing visible text, converting documents to Markdown, extracting tables, and preserving enough layout information to support downstream document processing.
The model is identified by the official checkpoint name deepseek-ai/DeepSeek-OCR-2. It is distributed under the Apache 2.0 license and is designed primarily for self-hosted inference. That distinction matters: DeepSeek-OCR 2 is a downloadable model checkpoint, not a separately documented DeepSeek consumer subscription feature or a model with a verified DeepSeek-hosted token price.
DeepSeek-OCR 2 belongs in the OCR and document-understanding part of DeepSeek's model catalog. Its purpose is narrower than that of a general conversational model. The model's value comes from processing visual documents and returning textual or layout-aware results, not from open-ended conversation, image creation, speech processing, or broad tool use.
Core OCR and document workflows
The model supports different prompting patterns for different extraction goals. A plain OCR prompt is intended to retrieve visible document text without placing as much emphasis on formatting. A grounding-oriented prompt can produce Markdown together with reference and detection tags that describe document-region coordinates.
This makes DeepSeek-OCR 2 useful when a pipeline needs more than a raw block of recognized text. For example, a scanned invoice may need its text transcribed while retaining approximate relationships between headings, line items, totals, and other regions. A report may need to be converted into Markdown for indexing or editing. A table may need to be extracted in a form that can be reviewed and transformed by later software.
According to the official repository materials, example workflows include single-image inference, Markdown conversion, PDF processing, batch evaluation, and streaming output through local serving infrastructure. These examples indicate that the model can be used as one component in a document-processing system rather than only as an interactive demonstration.
Layout output should be treated as useful extraction metadata, not as a guarantee of perfect document reconstruction. Documents with dense pages, small fonts, complex nested tables, unusual typography, or multiple columns should be tested on representative samples before being used in production.
Architecture and verified specifications
The published architecture combines several components: a SAM ViT-B vision encoder, a Qwen2-based hybrid attention encoder, an MLP projector, and a DeepSeek-V2 mixture-of-experts language component. The visual design uses bidirectional attention for image tokens and causal attention for query tokens. These details are relevant to implementers because the model is not simply a text-only language model with a basic image adapter; its checkpoint and inference code expect a multimodal processing path.
The checkpoint contains approximately 3 billion parameters and uses bfloat16 weights in the published configuration. Its language configuration specifies an 8,192-position maximum position setting. This is the documented configuration value, but it should not automatically be interpreted as a separately guaranteed maximum for generated output. The supplied research does not identify a distinct maximum-output-token limit.
Image processing uses dynamic resolution. The reference implementation describes a global 1,024-by-1,024 view and optional cropped 768-by-768 regions. Actual memory use and processing behavior can vary with image dimensions, crop selection, precision, batching, and the serving framework. Users should therefore regard these values as part of the reference processing design rather than as a promise that every document will be handled identically.
| Specification | Verified detail |
|---|---|
| Model type | OCR-specialized vision-language model |
| Parameters | Approximately 3 billion |
| Checkpoint | deepseek-ai/DeepSeek-OCR-2 |
| License | Apache 2.0 |
| Documented language context configuration | 8,192 positions |
| Documented maximum output tokens | Not identified |
| Primary deployment | Self-hosted Transformers, vLLM, SGLang, or compatible tools |
Inputs, outputs, and capabilities
DeepSeek-OCR 2 accepts text prompts and images. Its principal output is text, including ordinary OCR results, Markdown, and grounding-oriented coordinate tags. It does not directly generate images, audio, video, speech, embeddings, or other non-text media according to the supplied model data.
The model can be described as multimodal because it combines visual input with language processing. That does not make it a general multimodal assistant. There is no verified support in the supplied research for web search, function calling, agent actions, or a JSON-specific output mode. Streaming is documented in the local serving examples, but streaming output is a delivery method rather than a separate reasoning or tool-use capability.
Reasoning and coding are not the model's focus. Comparative editorial scores in the supplied data rate both reasoning and coding at 2, but those are not provider-published benchmarks or quality guarantees. They reflect the model's narrow OCR purpose: it may produce text that can later be processed by code, but it should not be selected as a general reasoning or software-development model.
Deployment and hardware considerations
The official deployment path loads the model from Hugging Face with custom model code through Transformers. The repository also documents vLLM-oriented scripts for image inference, concurrent PDF processing, benchmark evaluation, and streaming. Hugging Face documentation identifies additional serving paths such as vLLM, SGLang, Docker, and local application integrations.
The reference environment uses a modern NVIDIA CUDA setup. The published repository tested CUDA 11.8, PyTorch 2.6.0, FlashAttention 2.7.3, and a compatible Transformers environment. These versions describe the tested setup rather than an unconditional requirement for every installation. Compatibility can change as the model code, framework versions, and hardware ecosystem evolve.
There is no single hardware requirement supplied for all use cases. Memory and throughput will depend on numerical precision, image resolution, the number of page crops, batch size, concurrent requests, and whether the model is run interactively or through a serving layer. Page splitting and resolution adjustments may be necessary for long or visually dense documents.
Pricing and availability
The canonical checkpoint is publicly available from Hugging Face and can be run locally under the Apache 2.0 license. No official DeepSeek-hosted API price was identified for this exact OCR 2 checkpoint. As a result, there is no verified per-token input price, output price, subscription tier, or hosted maximum-output allowance to report for this model.
Self-hosting does not mean that processing is free. Operators still pay for GPU hardware, cloud instances, storage, electricity, monitoring, and engineering work. The economic advantage is control over deployment and the ability to choose infrastructure, batch documents, and keep processing within an organization's environment. The actual cost per page will vary substantially with hardware utilization and document complexity.
Strengths and limitations
Main strengths
- Specialized document focus: The model is built for OCR and document understanding rather than being adapted from a general chat workflow.
- Layout-aware extraction: Grounding-oriented prompts can return Markdown and coordinate-related tags, which may help downstream document parsing.
- Open deployment: The Apache 2.0 checkpoint can be downloaded and run locally, with documented support for common inference frameworks.
- Flexible document processing: Reference examples cover images, PDFs, batch evaluation, and streaming output.
- Potential infrastructure control: Local operation can reduce dependence on a particular hosted OCR endpoint and may help organizations keep document processing in their own environment.
Important limitations
- Not a general assistant: It is not positioned for broad conversation, general-purpose reasoning, image generation, speech, or embeddings.
- No verified managed API tariff: Users seeking a conventional hosted endpoint with predictable model-specific pricing will need another service or must arrange hosting themselves.
- Output quality is document-dependent: Small text, dense layouts, complex tables, and long multi-page jobs may need preprocessing, page splitting, prompt changes, or repeated validation.
- Output limits are not fully specified: The 8,192 configuration value describes the language-model position setting; the supplied research does not provide a separate maximum generated-output limit.
- Operational burden: Self-hosting requires compatible CUDA and framework setup, hardware capacity, model maintenance, and quality monitoring.
When to choose DeepSeek-OCR 2
Choose DeepSeek-OCR 2 when the central task is local or self-managed visual document processing. It is a reasonable candidate for scanned-document transcription, invoice and form extraction, table-oriented workflows, document indexing, PDF-to-Markdown conversion, and applications that need to inspect approximate page regions along with recognized text.
It is especially relevant when an organization wants an open-weight checkpoint and is prepared to manage its own inference environment. The model may also fit batch workloads where an operator can tune resolution, concurrency, prompts, and validation rules for a known document collection.
A managed OCR API may be more appropriate when the priority is a ready-to-use service, predictable hosted billing, vendor support, or minimal infrastructure work. A general-purpose vision-language model may be a better choice when OCR is only one small part of a broader assistant that must reason over documents, call tools, answer questions, or generate content across multiple modalities. A dedicated document-parsing system may be preferable when exact table reconstruction, strict schema compliance, or legally significant transcription requires specialized guarantees that this checkpoint does not provide.
DeepSeek-OCR 2 should therefore be evaluated as an OCR engine and document-understanding component, not against general chat models on unrelated tasks. A useful evaluation set should include the actual page layouts, languages, font sizes, table structures, scan quality, and output formats that the intended application will encounter.
Bottom line
DeepSeek-OCR 2 is a focused, approximately 3-billion-parameter open-weight model for turning document images into text and layout-aware representations. Its Apache 2.0 license, local deployment options, PDF and batch examples, and grounding-oriented output make it attractive for developers building controlled OCR pipelines. Its trade-off is equally clear: users must operate and evaluate the system themselves, and the supplied research does not establish a managed API price, a separate maximum-output limit, or general-purpose assistant capabilities.