Azure AI OCR (Mistral, Cohere Parse)

Overview

Property	Details
Description	Azure AI OCR provides document intelligence capabilities powered by Mistral and Cohere Parse, enabling text extraction from PDFs and images
Provider Route on LiteLLM	azure_ai/
Supported Operations	/ocr
Link to Provider Doc	Azure AI ↗

Extract text from documents and images using Azure AI's OCR models, powered by Mistral. Cohere Parse deployments are covered below.

Quick Start

LiteLLM SDK

SDK Usage

import litellm
import os

# Set environment variables
os.environ["AZURE_AI_API_KEY"] = ""
os.environ["AZURE_AI_API_BASE"] = ""

# OCR with PDF URL
response = litellm.ocr(
    model="azure_ai/mistral-document-ai-2505",
    document={
        "type": "document_url",
        "document_url": "https://example.com/document.pdf"
    }
)

# Access extracted text
for page in response.pages:
    print(page.text)

LiteLLM PROXY

proxy_config.yaml

model_list:
  - model_name: azure-ocr
    litellm_params:
      model: azure_ai/mistral-document-ai-2505
      api_key: "os.environ/AZURE_AI_API_KEY"
      api_base: "os.environ/AZURE_AI_API_BASE"
    model_info:
      mode: ocr

Document Types

Azure AI OCR supports both PDFs and images.

PDF Documents

PDF OCR

response = litellm.ocr(
    model="azure_ai/mistral-document-ai-2505",
    document={
        "type": "document_url",
        "document_url": "https://example.com/document.pdf"
    }
)

Image Documents

Image OCR

response = litellm.ocr(
    model="azure_ai/mistral-document-ai-2505",
    document={
        "type": "image_url",
        "image_url": "https://example.com/image.png"
    }
)

Base64 Encoded Documents

Base64 PDF

import base64

# Read and encode PDF
with open("document.pdf", "rb") as f:
    pdf_base64 = base64.b64encode(f.read()).decode()

response = litellm.ocr(
    model="azure_ai/mistral-document-ai-2505",
    document={
        "type": "document_url",
        "document_url": f"data:application/pdf;base64,{pdf_base64}"
    }
)

Supported Parameters

All Parameters

response = litellm.ocr(
    model="azure_ai/mistral-document-ai-2505",
    document={                           # Required: Document to process
        "type": "document_url",
        "document_url": "https://..."
    },
    include_image_base64=True,           # Optional: Include base64 images
    pages=[0, 1, 2],                     # Optional: Specific pages to process
    image_limit=10                       # Optional: Limit number of images
)

Response Format

Response Structure

# Response has the following structure
response.pages          # List of pages with extracted text
response.model          # Model used
response.object         # "ocr"
response.usage_info     # Token usage information

# Access page content
for page in response.pages:
    print(f"Page {page.page_number}:")
    print(page.text)

Async Support

Async Usage

import litellm

response = await litellm.aocr(
    model="azure_ai/mistral-document-ai-2505",
    document={
        "type": "document_url",
        "document_url": "https://example.com/document.pdf"
    }
)

Important Notes

URL Conversion

Azure AI OCR endpoints don't have internet access. LiteLLM automatically converts public URLs to base64 data URIs before sending requests to Azure AI.

Cohere Parse

Azure AI Foundry also serves Cohere Parse through the same /ocr endpoint. Use azure_ai/<deployment name>: a deployment whose name contains both cohere and parse (the catalog's default name Cohere-parse-v5 does) is sent to the Cohere Parse API on your Foundry resource, at {api_base}/providers/cohere/v2/parse. Other names keep routing to Mistral OCR, so keep cohere and parse in the deployment name if you rename it.

Parse accepts image_url documents only, an image URL or a base64 data:image/... URI. PDFs and document_url inputs are rejected with a 400 before anything is sent to Azure. Foundry cannot fetch external URLs, so LiteLLM downloads a remote image and sends it inline as a data URI, the same conversion it applies for the Mistral models above.

LiteLLM SDK

Cohere Parse on Azure AI

import litellm
import os

os.environ["AZURE_AI_API_KEY"] = ""
os.environ["AZURE_AI_API_BASE"] = "https://<resource>.services.ai.azure.com"

response = litellm.ocr(
    model="azure_ai/Cohere-parse-v5",
    document={
        "type": "image_url",
        "image_url": "https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/ocr/receipt.png",
    },
    output_format="markdown",
)

for page in response.pages:
    print(page.markdown)
print(response.usage_info.pages_processed)

LiteLLM PROXY

proxy_config.yaml

model_list:
  - model_name: azure-cohere-parse
    litellm_params:
      model: azure_ai/Cohere-parse-v5
      api_key: "os.environ/AZURE_AI_API_KEY"
      api_base: "os.environ/AZURE_AI_API_BASE"

Test request

curl http://0.0.0.0:4000/v1/ocr \
  -H "Authorization: Bearer sk-1234" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "azure-cohere-parse",
    "document": {
      "type": "image_url",
      "image_url": "https://raw.githubusercontent.com/mistralai/cookbook/refs/heads/main/mistral/ocr/receipt.png"
    }
  }'

output_format accepts markdown (default) or blocks, and req_format: native returns Cohere's own response body instead of the LiteLLM OCR shape. Cost tracking bills usage_info.pages_processed at the per-page price in the model cost map.

The model cost map prices azure_ai/Cohere-parse-v5 at Cohere's published rate of $1.50 per 1,000 pages, the price the Foundry catalog links to for this model.

Health checks (/health and the Admin UI's Test Connection button) send Parse a small PNG instead of the PDF used for Mistral OCR. Each probe is a real one-page Parse call, so it bills one page per deployment per check. The ocr probe mode and the per-page price are both looked up in the model cost map under Cohere-parse-v5; a deployment under any other name needs model_info: {mode: ocr, base_model: azure_ai/Cohere-parse-v5} in its model_list entry, the same mode and base_model convention every other Azure model uses, so health checks probe it as OCR and spend tracking finds the Parse price instead of recording $0.

Supported Models

• mistral-document-ai-2505 - Latest Mistral OCR model on Azure AI
• Cohere-parse-v5 - Cohere Parse, image documents only

Use the Azure AI provider prefix: azure_ai/<model-name>