deepseek-ai/deepseek-ocr-2

Generative document OCR (vLLM). Accepts one image_url or a document_url PDF; no text-only input. Default method: markdown. Three methods return the page as a string; grounding_ocr instead locates a phrase and returns region boxes.

Output by method

Method	Payload kind	Image `content.object`	Payload
`markdown`	markdown	none	Reading-order Markdown string (default)
`ocr`	markdown	none	Markdown via the grounded conversion prompt; grounding tags are stripped from the output
`free_ocr`	markdown	none	Free-form plain read of the page
`grounding_ocr`	json	`deepseek_ocr_2.grounding_ocr.regions`	Region items with `bbox_xywh` and the matched `text`

Text mode emits the string alone for one image, and one <document> / <page> block per PDF page. In JSON mode an image content is the string, or the region container for grounding_ocr, and a document page content is always document.page.blocks. See Methods & Response Format. The model grounds on a fixed 0-1000 grid; the Gateway normalizes every box to 0-1 against the image or rasterized page, at precision decimals (default 4). Grounding regions carry bbox_xywh and text only: no polygon and no score.

Request

from openai import OpenAI

client = OpenAI(
    base_url="https://gateway.vlm.run/v1/openai",
    api_key="<VLMRUN_API_KEY>",
)

response = client.chat.completions.create(
    model="deepseek-ai/deepseek-ocr-2",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "document_url",
                    "document_url": {
                        "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/finance.sec-filings/tsla-8k.pdf"
                    },
                },
            ],
        }
    ],
    extra_body={"method": "markdown"},
)

print(response.choices[0].message.content)

curl https://gateway.vlm.run/v1/openai/chat/completions \
  -X POST \
  -H "Authorization: Bearer $VLMRUN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-ai/deepseek-ocr-2",
    "method": "markdown",
    "messages": [
      {
        "role": "user",
        "content": [
          {
            "type": "document_url",
            "document_url": {
              "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/finance.sec-filings/tsla-8k.pdf"
            }
          }
        ]
      }
    ]
  }'

Response

Text mode
JSON mode

One <document> block per PDF, one <page> block per page, each carrying format="markdown":

<document file_name="tsla-8k.pdf" file_hash="sha256:1b7f…" file_bytes="182417" mimetype="application/pdf" num_pages="2" dpi="96">
<page page_index="0" format="markdown" page_width="1700" page_height="2200">
# Annual Report 2024

## Overview

The company reported revenue of $1.2M.
</page>
<page page_index="1" format="markdown" page_width="1700" page_height="2200">
| Metric | 2023 | 2024 |
| --- | --- | --- |
| Revenue | 0.9M | 1.2M |
</page>
</document>

A single image returns the Markdown string alone, with no wrapper.

One document entry; each page’s content is one Markdown block:

{
  "model": "deepseek-ai/DeepSeek-OCR-2",
  "method": "markdown",
  "data": [
    {
      "object": "document",
      "file_name": "tsla-8k.pdf",
      "file_hash": "sha256:1b7f...",
      "file_bytes": 182417,
      "mimetype": "application/pdf",
      "num_pages": 2,
      "dpi": 96,
      "pages": [
        {
          "object": "document.page",
          "page_index": 0,
          "page_width": 1700,
          "page_height": 2200,
          "content": {
            "object": "document.page.blocks",
            "items": [
              {"index": 0, "text": "# Annual Report 2024\n\n## Overview\n\nThe company reported revenue of $1.2M."}
            ]
          }
        }
      ]
    }
  ]
}

Grounding

grounding_ocr answers “where does this appear on the page”. Pass the full grounding prompt via method_params.prompt, with the phrase to locate wrapped in the model’s ref markers:

response = client.chat.completions.create(
    model="deepseek-ai/deepseek-ocr-2",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "image_url",
                    "image_url": {
                        "url": "https://storage.googleapis.com/vlm-data-public-prod/hub/examples/document.receipt/playground/2.jpg"
                    },
                },
            ],
        }
    ],
    extra_body={
        "method": "grounding_ocr",
        "method_params": {"prompt": "<image>\nLocate <|ref|>Total Amount<|/ref|> in the image."},
    },
)

The reply is the region container, alone in text mode and wrapped in data in JSON mode:

{"object": "deepseek_ocr_2.grounding_ocr.regions", "items": [{"bbox_xywh": [0.102, 0.08, 0.893, 0.06], "text": "Annual Report 2024"}, {"bbox_xywh": [0.102, 0.16, 0.893, 0.08], "text": "Overview"}]}

On a PDF each page’s content is document.page.blocks, with one block per located region carrying the same bbox_xywh and text.

Aliases

deepseek-ai/deepseek-ocr-2 (preferred), deepseek-ocr-2, and deepseek-ai/DeepSeek-OCR-2 all resolve to this model.

Overview

Guides

API Reference

Misc

deepseek-ai/deepseek-ocr-2

Output by method

Request

Response

Grounding

Aliases

​Output by method

​Request

​Response

​Grounding

​Aliases

Output by method

Request

Response

Grounding

Aliases