Tech

Rails testing on autopilot: Building an agent that writes what developers won't

In most large Rails monoliths, organizations prioritize writing new features over writing tests for them. Over time, more and more code goes untested, forcing teams to spend more time debugging painful bugs.

Workflows for work that runs the business

Today, we're releasing Workflows in public preview. Workflows is the orchestration layer for enterprise AI. It brings the durability, observability, and fault tolerance required to move AI-powered processes from proof of concept to production reliably. Organizations like ASML, ABANCA, CMA-CGM, France Travail, La Banque Postale, Moeve, and many more are already running Workflows to automate critical processes.

Introducing Mistral OCR 3

Breakthrough performance: 74% overall win rate over Mistral OCR 2 on forms, scanned documents, complex tables, and handwriting.

Unlocking the potential of vision language models on satellite imagery through fine-tuning

Fine-tuning foundation models is transforming how we apply AI to real-world problems. By adapting pre-trained models to specific domains, we can unlock dramatically better performance on specialized tasks. Today, we’re excited to share how fine-tuning Pixtral-12B on satellite imagery leads to significant improvements over the base model, showcasing the power of domain-specific adaptation.

Mistral Small 3

Today we're introducing Mistral Small 3, a latency-optimized 24B-parameter model released under the Apache 2.0 license.

Make Memory work for you.

As conversational AIs get more capable, our expectations grow with them. We don’t just want faster answers, we want tools that remember, adapt, and fit the way we work. That’s where Memories (beta) come in. And with it, new questions: What should an AI remember? How should it recall? And what does it take for you to trust it?

Mistral Saba

Making AI ubiquitous requires addressing every culture and language. As AI proliferates globally, many of our customers worldwide have expressed a strong desire for models that are not just fluent but native to regional parlance. While larger, general-purpose models are often proficient in several languages, they lack linguistic nuances, cultural background, and in-depth regional knowledge required to serve use cases with strong regional context.

Leanstral: Open-Source foundation for trustworthy vibe-coding

AI agents have proven to be highly capable tools at code generation. Yet, as we push these models to high-stakes domains, ranging from frontier research mathematics to mission-critical software, we encounter a scaling bottleneck: the human review. The time and specialized expertise required to manually verify become the primary impedance of engineering velocity.

Our contribution to a global environmental standard for AI

At Mistral AI, our mission is to bring artificial intelligence in everyone’s hands. For this purpose, we have consistently advocated for openness in AI, with a unique focus on empowering organizations that want to own their AI future.

Purr-fectly informed

At Mistral AI, our mission is to place AI in everyone's hands. This means democratizing access to advanced AI interfaces, making trustworthy information accessible, and ensuring that everyone can use AI with confidence and trust.

Connect the dots: Build with built-in and custom MCPs in Studio

Today we are releasing Connectors in Studio to unblock developers building highly customised AI applications grounded in enterprise data. All built-in connectors, as well as custom MCPs, are now available via API/SDK to be used with all model and agent calls.

Mistral AI partners with NVIDIA to accelerate open frontier models

Shared expertise: By pooling resources and knowledge, we reduce duplication and accelerate progress across the AI ecosystem.