OCR

LLMGateway exposes a dedicated /v1/ocr endpoint for optical character recognition. It extracts text, tables, and layout from PDFs and images and returns them as clean markdown, one entry per page.

Use it when you want to:

• Turn scanned PDFs or photos into machine-readable markdown
• Pull structured text out of receipts, invoices, forms, or screenshots
• Feed document contents into a downstream model or RAG pipeline

For the full request and response schema, see the API reference.

Endpoint

POST https://api.llmgateway.io/v1/ocr

Authenticate with your LLMGateway API key:

-H "Authorization: Bearer $LLM_GATEWAY_API_KEY"

The current model is mistral-ocr-latest, billed at $4 per 1,000 pages processed.

Document Input

The document field accepts either a document URL (PDF) or an image:

• { "type": "document_url", "document_url": "https://…/file.pdf" }
• { "type": "image_url", "image_url": "https://…/image.png" }

Both document_url and image_url accept a public URL or a base64 data URL (data:application/pdf;base64,… / data:image/png;base64,…). The image_url field may also be passed as an object: { "url": "…" }.

Scoping pages

By default the entire document is processed and every page is billed. Use the optional pages field to restrict (and cap the cost of) a request:

• A list of zero-based indices: "pages": [0, 1, 2]
• A range string: "pages": "0-4"

curl

Document URL

curl -X POST "https://api.llmgateway.io/v1/ocr" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral-ocr-latest",
    "document": {
      "type": "document_url",
      "document_url": "https://arxiv.org/pdf/2201.04234"
    }
  }'

Only specific pages

curl -X POST "https://api.llmgateway.io/v1/ocr" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral-ocr-latest",
    "document": {
      "type": "document_url",
      "document_url": "https://arxiv.org/pdf/2201.04234"
    },
    "pages": "0-4"
  }'

Image input

curl -X POST "https://api.llmgateway.io/v1/ocr" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral-ocr-latest",
    "document": {
      "type": "image_url",
      "image_url": "https://example.com/receipt.png"
    }
  }'

Inline (base64) document

BASE64_PDF=$(base64 -i invoice.pdf)

curl -X POST "https://api.llmgateway.io/v1/ocr" \
  -H "Authorization: Bearer $LLM_GATEWAY_API_KEY" \
  -H "Content-Type: application/json" \
  -d "{
    \"model\": \"mistral-ocr-latest\",
    \"document\": {
      \"type\": \"document_url\",
      \"document_url\": \"data:application/pdf;base64,${BASE64_PDF}\"
    }
  }"

Response Shape

{
	"pages": [
		{
			"index": 0,
			"markdown": "# Document title\n\nExtracted body text…",
			"images": [],
			"dimensions": { "dpi": 200, "height": 2200, "width": 1700 }
		}
	],
	"model": "mistral-ocr-latest",
	"document_annotation": null,
	"usage_info": {
		"pages_processed": 1,
		"doc_size_bytes": 125344
	}
}

Each entry in pages carries the markdown for one page. usage_info.pages_processed reflects exactly how many pages were billed for the request.

Billing

OCR is billed per page processed, not per token. A request that processes 12 pages bills 12 × $0.004 = $0.048. Scoping a request with pages reduces both the work and the cost.

OCR Models Use This Endpoint, Not Chat

OCR models are not chat models and cannot be called through /v1/chat/completions — doing so returns a 400 pointing you here. Send OCR requests to /v1/ocr.