
Mistral OCR 4 delivers enterprise document AI with 170-language support, bounding boxes, and self-hosted deployment.
Mistral OCR 4 is a document processing model that extracts text from images and documents while also identifying where text is located, what type of content it represents, and how confident the model is in its extraction. The model supports 170 languages and can be deployed on a single container for organizations that need to keep document data within their own infrastructure. According to the source, independent annotators preferred OCR 4 over competing systems, and it achieves lower costs and faster processing speeds compared to some alternatives. The structured output is designed to support downstream tasks like retrieval-augmented generation, document-based agent actions, and content indexing pipelines.

Gradium today released two real-time speech translation models: stt-translate and s2s-translate. Both run across five languages and stream results live in the browser. Gradium claims a better accuracy-latency tradeoff than gpt-realtime-translate and gemini-3.5-live-translate. It also adds output voice control, including cloning, that gpt-realtime-translate lacks. TL;DR Gradium launched two real-time speech translation models: stt-translate (speech → text) and s2s-translate (speech → s

Today, Mistral AI released OCR 4, its latest document-understanding model. This new release adds bounding boxes, block classification, and inline confidence scores alongside extracted text. It supports 170 languages across 10 language groups and runs in a single container for fully self-hosted deployments. OCR 4 also serves as an ingestion component for enterprise search, RAG, and domain-specific retrieval pipelines. TL;DR OCR 4 returns bounding boxes, typed-block labels, and per-word c

Datalab has released lift, a 9B open-weights vision model for structured extraction. You pass it a JSON schema, and it returns a JSON object that matches. The model reads PDFs and images directly, then decodes against your schema. This is Datalab’s first model built purely for extraction. The team already ships open-source OCR tools: chandra, marker, and surya. lift extends that work into schema-driven field extraction. lift scores 90.2% field accuracy on Datalab’s 225-documen
Want to go deeper than the news? Explore live, cohort-based AI courses taught by practitioners.
Browse AI courses on Maven