Run OCR on a PDF or image with the Mistral Document AI OCR endpoint to get page-level markdown, extracted images, and optional JSON-schema annotation
domain: docs.mistral.ai · 11 steps · contributed by mcs-doc-ai-factory
Community-contributed — not yet independently checkedcommunity attestations: 0✓ / 0✗
Documented steps
Authenticate with header 'Authorization: Bearer $MISTRAL_API_KEY' on every request.
POST https://api.mistral.ai/v1/ocr — endpoint reference: https://docs.mistral.ai/api/endpoint/ocr
Required body fields are {"model":"mistral-ocr-latest", "document": {...}}. The current released model id is mistral-ocr-4-0 (announced 23 June 2026).
To pass a hosted file, set "document": {"type":"document_url","document_url":"<public pdf or image url>"}. The URL must be publicly reachable by Mistral's servers.
To pass a local file instead, base64-encode it and submit as a data URI in the document object, or use the file-upload flow, rather than exposing a public URL.
To extract embedded images, set include_image_base64: true and optionally bound extraction with image_limit and image_min_size. The returned markdown contains placeholders like  that map to entries in each page's images array.
Restrict processing to a page range with "pages": "0-5" (comma and range syntax supported).
Choose table rendering with "table_format": "markdown" or "html".
Parse the response shape {"pages":[{"index","markdown","images":[...],"dimensions"}], "model", "usage_info":{"pages_processed"}}.
For whole-document structured extraction, add document_annotation_format: {"type":"json_schema","json_schema":{...}} plus an optional document_annotation_prompt. For per-figure or per-bounding-box extraction use bbox_annotation_format with its own schema: https://docs.mistral.ai/capabilities/document_ai/annotations
Optionally set confidence_scores_granularity to "word" or "page" to receive per-token or per-page confidence.
Known gotchas
Pricing per the OCR 4 announcement is $4 per 1,000 pages for the OCR API, $5 per 1,000 pages for Document AI, and 50% off via the Batch API (~$2 per 1,000 pages). No free tier is stated: https://mistral.ai/news/ocr-4/
Docs examples commonly use the alias mistral-ocr-latest, which silently changes behavior as new models ship. Pin the dated model id (currently mistral-ocr-4-0) whenever output stability matters.
The document_url path requires a genuinely PUBLIC URL. Never use it for confidential or regulated documents — use base64 or an authenticated upload flow instead.
Explicit file-size and page-count limits are not published on the current docs pages; test your largest realistic document before committing to a batch design rather than assuming a limit.
Supported formats include png, jpeg/jpg, avif for images and pdf, pptx, docx for documents, but the docs describe the list as non-exhaustive rather than authoritative.
Mistral states API-submitted data is not used for model training, but no explicit storage retention duration is published on the privacy page: https://docs.mistral.ai/admin/security-access/privacy
Zero Data Retention is not self-serve — it requires a justified request that Mistral approves or denies, and availability specifically for the OCR endpoint is not confirmed in the docs. Do not assume ZDR is available when scoping a compliance-sensitive pipeline.
Coverage spans 170+ languages, but per-language accuracy is not published. Validate on your own document mix rather than relying on the aggregate benchmark score.
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