Mistral OCR: New Document Understanding API with State-of-the-Art Benchmark Performance
Mistral AI has released Mistral OCR, an Optical Character Recognition API designed for deep document understanding, handling text, tables, equations, images, and complex layouts from PDFs and images. The model claims top benchmark scores across math, multilingual, scanned, and table categories, outperforming Google Document AI, Azure OCR, Gemini 1.5/2.0, and GPT-4o on an internal test set. It is priced at 1000 pages per dollar (with batch inference doubling that), available via la Plateforme API today, and is already deployed as the default document understanding model in Le Chat. A selective self-hosting option is offered for organizations with sensitive data requirements.
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Mistral OCR 3: New Frontier in Document Processing Accuracy and Efficiency
Mistral AI has released Mistral OCR 3 (model ID: mistral-ocr-2512), claiming a 74% overall win rate over its predecessor Mistral OCR 2 across forms, scanned documents, complex tables, and handwriting. The model supports markdown output with HTML-based table reconstruction and is priced at $2 per 1,000 pages ($1 with Batch API). It now powers the Document AI Playground in Mistral AI Studio, offering a drag-and-drop interface for parsing PDFs and images into text or structured JSON.
Mistral Medium 3: Frontier-Class Performance at 8x Lower Cost
Mistral AI has released Mistral Medium 3, a new enterprise-focused language model priced at $0.4/$2 per million input/output tokens. The model claims to achieve 90%+ of Claude Sonnet 3.7's benchmark performance while undercutting cost leaders like DeepSeek v3, and outperforming open models including Llama 4 Maverick. It supports hybrid, on-premises, and in-VPC deployment on as few as four GPUs, and is available immediately on Mistral La Plateforme and Amazon SageMaker, with additional cloud platforms coming soon. The announcement also teases an upcoming large open-weights model release.
Mistral Small 3.1: Multimodal, 128k Context, Apache 2.0 Open-Weight Model
Mistral AI releases Mistral Small 3.1, a ~24B parameter model with multimodal understanding, 128k token context window, and claimed best-in-class performance among small models, outperforming Gemma 3 and GPT-4o Mini on text, multimodal, and multilingual benchmarks. The model runs on a single RTX 4090 or 32GB RAM Mac at 150 tokens/second and is released under Apache 2.0 license with both base and instruct checkpoints. It is available on HuggingFace, Mistral's La Plateforme API, and Google Cloud Vertex AI, with NVIDIA NIM and Azure AI Foundry support coming soon. The release targets enterprise and on-device use cases including document verification, agentic workflows, and domain fine-tuning.
Mistral AI Releases Mistral Large, Claims Second-Best API Model After GPT-4
Mistral AI has released Mistral Large, its most capable model to date, claiming second place among API-accessible models behind GPT-4 on standard benchmarks including MMLU, HellaSwag, and coding/math evals. The model features a 32K context window, native fluency in five European languages, function calling, and constrained output mode. Simultaneously, Mistral is launching a new Mistral Small optimized for latency, restructuring its endpoint lineup, and announcing Microsoft Azure as its first major distribution partner. This marks Mistral's first significant commercial partnership and expansion beyond its own infrastructure.
Mistral AI Releases Mistral Small v24.09, Free API Tier, and Pixtral 12B Vision on le Chat with Broad Price Cuts
Mistral AI announced a multi-part release on September 17, 2024: a free tier for la Plateforme API, significant price reductions across its model family (up to 80% for Mistral Small and Codestral), an updated Mistral Small v24.09 (22B parameters, improved alignment and reasoning), and the availability of Pixtral 12B vision capabilities on le Chat. Pixtral 12B, released under Apache 2.0, supports images of any size without text performance degradation and is now accessible for free on le Chat. The pricing updates also apply to cloud partner deployments on Azure AI Studio, Amazon Bedrock, and Google Vertex AI.
Mistral AI Launches La Plateforme: First API Endpoints in Early Access
Mistral AI opened beta access to its first developer platform, La Plateforme, offering three generative text endpoints (mistral-tiny, mistral-small, mistral-medium) and an embedding endpoint. Mistral-tiny serves Mistral 7B Instruct v0.2, mistral-small serves Mixtral 8x7B, and mistral-medium serves an unreleased prototype model scoring 8.6 on MT-Bench. The platform also introduces Mistral-embed with a 1024-dimension embedding model achieving 55.26 on MTEB. The API follows OpenAI-compatible chat interface specifications and is ramping toward general availability.
Mistral Small 4: Unified Multimodal, Reasoning, and Coding MoE Model Released Under Apache 2.0
Mistral AI has released Mistral Small 4, a 119B-parameter Mixture-of-Experts model (6B active per token) that unifies capabilities previously split across Magistral (reasoning), Pixtral (multimodal), and Devstral (coding agents) into a single open-weights model. The model features a 256k context window, configurable reasoning effort via a `reasoning_effort` parameter, native text and image input support, and is released under Apache 2.0. Mistral claims 40% latency reduction and 3x throughput improvement over Mistral Small 3, with benchmark results showing competitive performance against GPT-OSS 120B and Qwen models while producing significantly shorter outputs. The release includes day-0 availability as an NVIDIA NIM and support across vLLM, llama.cpp, SGLang, and Transformers.
Mistral AI Founding Manifesto and Mistral 7B Release
Mistral AI published its founding mission statement alongside the release of Mistral 7B, a 7-billion-parameter open-weights language model released under Apache 2.0. The model claims to outperform all available open models up to 13B parameters on standard English and code benchmarks, produced in three months from a standing start. The post articulates Mistral's strategic thesis: open-weight models will outcompete proprietary black-box APIs for most enterprise use cases, drawing analogies to Linux, WebKit, and Kubernetes. The company signals intent to release progressively larger frontier models while building a commercial offering around on-premise and VPC deployment.