French AI startup Mistral on Tuesday announced its new Mistral 3 family of open-weight models. This includes the release of 10 models, including a large Frontier model with multimodal and multilingual capabilities, and nine smaller models that are offline-enabled and fully customizable.
The announcement comes as Mistral, which develops open weight language models and Europe-focused AI chatbot Le Chat, appears to be catching up to some of Silicon Valley’s closed-source frontier models. The two-year-old startup, founded by former DeepMind and Meta researchers, has raised about $2.7 billion to date at a valuation of $13.7 billion. That’s an order of magnitude compared to competitors like OpenAI (raised $57 billion at a $500 billion valuation) and Anthropic (raised $45 billion at a $350 billion valuation).
But Mistral is trying to prove that bigger isn’t always better, especially for enterprise use cases.
“Sometimes our customers are willing to start very large. [closed] Guillaume Lampre, co-founder and chief scientist at Mistral, told TechCrunch: [more efficiently]”
“The reality is that the vast majority of enterprise use cases can be addressed with a smaller model, especially with fine tuning,” Lampl continued.
Early benchmark comparisons could be misleading, Lampl said, as Mistral’s smaller model lags far behind its closed-source competitors. A large-scale, closed-source model may offer better out-of-the-box performance, but the real benefits come when you customize it.
“In many cases, you can actually achieve performance that is equal to or better than a closed source model,” he says.
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Mistral’s large-scale frontier model, called Mistral Large 3, has caught up with some of the key features boasted by larger closed-source AI models such as OpenAI’s GPT-4o and Google’s Gemini 2, while also taking a beating with some openweight competitors. Large 3 is one of the first open frontier models to combine multimodal and multilingual capabilities, making it comparable to Meta’s Llama 3 and Alibaba’s Qwen3-Omni. Many other companies are now combining impressive large language models with discrete smaller multimodal models. This is something Mistral has done before with models like Pixtral and Mistral Small 3.1.
Large 3 also features a “grained expert mix” architecture with 41B active parameters and 675B total parameters, enabling efficient inference across a 256k context window. This design delivers both speed and functionality, allowing you to process long documents and act as an agent assistant for complex enterprise tasks. Mistral positions the Large 3 as suitable for document analysis, coding, content creation, AI assistants, and workflow automation.
With a new family of small models dubbed Mistral 3, Mistral is boldly claiming that small is not enough, it’s better.
The lineup includes nine different high-performance high-density models across three sizes (14B, 8B, and 3B parameters) and three variants. Base (pre-trained base model), Instruct (chat optimized for conversational and assistant-style workflows), and Reasoning (optimized for complex logic and analytical tasks).
According to Mistral, the product family gives developers and companies the flexibility to adapt models to exact performance, whether they are looking for raw performance, cost efficiency, or specialized functionality. The company claims that Ministeral 3 is more efficient and generates fewer tokens for comparable tasks, while achieving scores equal to or better than other open-class leaders. All variants support vision, handle context windows from 128K to 256K, and work in multiple languages.
A big part of the pitch is practicality. Lample emphasizes that because Ministeral 3 can run on a single GPU, it can be deployed on affordable hardware, from on-premises servers to laptops, robots, and other edge devices with limited connectivity. This is important not only for companies that store data in-house, but also for students seeking offline feedback and robotics teams working in remote environments. Lampl argues that increased efficiency directly translates into greater accessibility.
“It’s part of our mission to make AI accessible to everyone, especially people who don’t have access to the internet,” he said. “We don’t want AI to be controlled only by a few big labs.”
Several other companies are pursuing similar efficiency tradeoffs. Cohere’s latest enterprise model, Command A, also runs on only two GPUs, and the company’s AI agent platform, North, can run on only one GPU.
This kind of accessibility is driving Mistral’s expanded focus on physical AI. Earlier this year, the company began working to integrate smaller models into robots, drones and vehicles. Mistral is collaborating with Singapore’s Home Team Science and Technology Agency (HTX) on specialized models for robots, cybersecurity systems and fire protection. Joint research with German defense technology startup Hellsing on visual, language, and behavior models for drones. We have jointly developed an in-vehicle AI assistant with automaker Stellantis.
For Mistral, reliability and independence are as important as performance.
“If you use a competitor’s API, you’re going to be down for 30 minutes every two weeks, and if you’re a large company, you can’t afford that,” Rumple said.
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