/ocr

Feature	Supported
Cost Tracking	✅
Logging	✅ (Basic Logging not supported)
Load Balancing	✅
Supported Providers	mistral, azure_ai, vertex_ai

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LiteLLM follows the Mistral API request/response for the OCR API

LiteLLM Python SDK Usage

Quick Start

from litellm import ocr
import os

os.environ["MISTRAL_API_KEY"] = "sk-.."

response = ocr(
    model="mistral/mistral-ocr-latest",
    document={
        "type": "document_url",
        "document_url": "https://arxiv.org/pdf/2201.04234"
    }
)

# Access extracted text
for page in response.pages:
    print(f"Page {page.index}:")
    print(page.markdown)

Async Usage

from litellm import aocr
import os, asyncio

os.environ["MISTRAL_API_KEY"] = "sk-.."

async def test_async_ocr(): 
    response = await aocr(
        model="mistral/mistral-ocr-latest",
        document={
            "type": "document_url",
            "document_url": "https://arxiv.org/pdf/2201.04234"
        }
    )
    
    # Access extracted text
    for page in response.pages:
        print(f"Page {page.index}:")
        print(page.markdown)

asyncio.run(test_async_ocr())

Using Local Files

LiteLLM can read local files directly — no manual base64 encoding needed:

from litellm import ocr

# OCR with a local PDF file path
response = ocr(
    model="mistral/mistral-ocr-latest",
    document={
        "type": "file",
        "file": "/path/to/document.pdf"
    }
)

# OCR with a file object
response = ocr(
    model="mistral/mistral-ocr-latest",
    document={
        "type": "file",
        "file": open("document.pdf", "rb")
    }
)

# OCR with raw bytes
with open("document.pdf", "rb") as f:
    pdf_bytes = f.read()

response = ocr(
    model="mistral/mistral-ocr-latest",
    document={
        "type": "file",
        "file": pdf_bytes,
        "mime_type": "application/pdf"  # recommended for raw bytes (auto-detected from extension for file paths)
    }
)

The file field accepts:

• File path (str or pathlib.Path) — LiteLLM reads the file and detects the MIME type from the extension
• File object (binary file-like object) — e.g. open("doc.pdf", "rb")
• Raw bytes (bytes) — use mime_type to specify the content type

LiteLLM automatically converts file inputs to base64 data URIs internally, so all providers work seamlessly.

Using Base64 Encoded Documents

import base64
from litellm import ocr

# Encode PDF to base64
with open("document.pdf", "rb") as f:
    base64_pdf = base64.b64encode(f.read()).decode('utf-8')

response = ocr(
    model="mistral/mistral-ocr-latest",
    document={
        "type": "document_url",
        "document_url": f"data:application/pdf;base64,{base64_pdf}"
    }
)

Optional Parameters

response = ocr(
    model="mistral/mistral-ocr-latest",
    document={
        "type": "document_url",
        "document_url": "https://example.com/doc.pdf"
    },
    # Optional Mistral parameters
    pages=[0, 1, 2],              # Only process specific pages
    include_image_base64=True,     # Include extracted images
    image_limit=10,                # Max images to return
    image_min_size=100             # Min image size to include
)

LiteLLM Proxy Usage

LiteLLM provides a Mistral API compatible /ocr endpoint for OCR calls.

Setup

Add this to your litellm proxy config.yaml

model_list:
  - model_name: mistral-ocr
    litellm_params:
      model: mistral/mistral-ocr-latest
      api_key: os.environ/MISTRAL_API_KEY

Start litellm

litellm --config /path/to/config.yaml

# RUNNING on http://0.0.0.0:4000

Test request — JSON body

curl http://0.0.0.0:4000/v1/ocr \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral-ocr",
    "document": {
        "type": "document_url",
        "document_url": "https://arxiv.org/pdf/2201.04234"
    }
  }'

Test request — multipart file upload

Upload a file directly using multipart form data. No need to base64-encode the file yourself.

curl http://0.0.0.0:4000/v1/ocr \
  -H "Authorization: Bearer sk-1234" \
  -F "model=mistral-ocr" \
  -F "file=@/path/to/document.pdf"

You can also pass optional parameters as additional form fields:

curl http://0.0.0.0:4000/v1/ocr \
  -H "Authorization: Bearer sk-1234" \
  -F "model=mistral-ocr" \
  -F "[email protected]" \
  -F 'pages=[0,1,2]' \
  -F "include_image_base64=true"

Request/Response Format

info

LiteLLM follows the Mistral OCR API specification.

See the official Mistral OCR documentation for complete details.

Example Request

{
    "model": "mistral/mistral-ocr-latest",
    "document": {
        "type": "document_url",
        "document_url": "https://arxiv.org/pdf/2201.04234"
    },
    "pages": [0, 1, 2],              # Optional: specific pages to process
    "include_image_base64": True,     # Optional: include extracted images
    "image_limit": 10,                # Optional: max images to return
    "image_min_size": 100             # Optional: min image size in pixels
}

Request Parameters

Parameter	Type	Required	Description
model	string	Yes	The OCR model to use (e.g., "mistral/mistral-ocr-latest")
document	object	Yes	Document to process. Must contain type and the corresponding field
document.type	string	Yes	"document_url" for PDFs/docs, "image_url" for images, or "file" for local files
document.document_url	string	Conditional	URL or data URI to the document (required if type is "document_url")
document.image_url	string	Conditional	URL or data URI to the image (required if type is "image_url")
document.file	string/bytes/file	Conditional	File path, bytes, or file-like object (required if type is "file")
document.mime_type	string	No	Explicit MIME type for file inputs (auto-detected from extension if not provided)
pages	array	No	List of specific page indices to process (0-indexed)
include_image_base64	boolean	No	Whether to include extracted images as base64 strings
image_limit	integer	No	Maximum number of images to return
image_min_size	integer	No	Minimum size (in pixels) for images to include

Document Format Examples

For PDFs and documents (URL):

{
  "type": "document_url",
  "document_url": "https://example.com/document.pdf"
}

For images (URL):

{
  "type": "image_url",
  "image_url": "https://example.com/image.png"
}

For base64-encoded content:

{
  "type": "document_url",
  "document_url": "data:application/pdf;base64,JVBERi0xLjQKJ..."
}

For local files (SDK):

{"type": "file", "file": "/path/to/document.pdf"}
{"type": "file", "file": open("image.png", "rb")}
{"type": "file", "file": pdf_bytes, "mime_type": "application/pdf"}

For file uploads (Proxy — multipart form):

curl http://0.0.0.0:4000/v1/ocr \
  -H "Authorization: Bearer sk-1234" \
  -F "model=mistral-ocr" \
  -F "[email protected]"

Response Format

The response follows Mistral's OCR format with the following structure:

{
  "pages": [
    {
      "index": 0,
      "markdown": "# Document Title\n\nExtracted text content...",
      "dimensions": {
        "dpi": 200,
        "height": 2200,
        "width": 1700
      },
      "images": [
        {
          "image_base64": "base64string...",
          "bbox": {
            "x": 100,
            "y": 200,
            "width": 300,
            "height": 400
          }
        }
      ]
    }
  ],
  "model": "mistral-ocr-2505-completion",
  "usage_info": {
    "pages_processed": 29,
    "doc_size_bytes": 3002783
  },
  "document_annotation": null,
  "object": "ocr"
}

Response Fields

Field	Type	Description
pages	array	List of processed pages with extracted content
pages[].index	integer	Page number (0-indexed)
pages[].markdown	string	Extracted text in Markdown format
pages[].dimensions	object	Page dimensions (dpi, height, width in pixels)
pages[].images	array	Extracted images from the page (if include_image_base64=true)
model	string	The model used for OCR processing
usage_info	object	Processing statistics (pages processed, document size)
document_annotation	object	Optional document-level annotations
object	string	Always "ocr" for OCR responses

Supported Providers

Provider	Link to Usage
Mistral AI	Usage
Azure AI	Usage
Vertex AI	Usage