Introducing Mistral OCR 4

Mistral OCR 4 introduces breakthrough document parsing with bounding boxes, block classification, and inline confidence scores, supporting 170 languages and running in a single container for self-hosted deployments. It outperforms leading OCR systems in human evaluations and benchmarks while offering cost-efficient, high-throughput processing for enterprise search, RAG, and agentic workflows. Available via API or Document AI, it provides structured outputs for custom pipelines or no-code applications, with self-hosting options for data privacy.

Today, we're releasing Mistral OCR 4, featuring bounding boxes, block classification, and inline confidence scores alongside extracted text. The model supports 170 languages across 10 language groups, runs in a single container for fully self-hosted deployments, and serves as an ingestion component for enterprise search, RAG, and domain-specific retrieval pipelines. OCR 4 is a small, focused model, and this post covers what's new, how it performs on public and internal benchmarks, the known limitations of those benchmarks, and guidance on when to use the model API versus Document AI.

Breakthrough performance. Independent annotators prefer OCR 4 over every leading OCR and document-AI system tested, with win rates averaging 72%, alongside the top overall score on OlmOCRBench (85.20). See Benchmarks below for methodology and known scoring limitations.

Segmentation, not just text. Alongside the extracted text, OCR 4 returns bounding boxes, typed-block classification (titles, tables, equations, signatures, and more), and inline confidence scores. Bounding boxes, our most-requested capability, localize text for in-context highlighting and reliable data pipelines. At the same time, block types and confidence scores drive source-grounded citations, redactions, and human-in-the-loop verification.