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Key takeaways 

• Mistral 3 is designed for customization and privacy. 
• Its smaller multimodal models can run on single GPUs.
• Mistral hopes the models create "distributed intelligence." 

Another open-source model has joined the ever-expanding AI race, this time from boutique French AI lab Mistral -- and it's going small where most other labs go big. 

Mistral 3, a family of four open-source models released by the company on Tuesday, offers "unprecedented flexibility and control for enterprises and developers," according to the announcement. The suite includes a large model, two mid-size models, and a smaller edition, aiming to address a wider variety of needs. 

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"This spectrum of models further extends our customers' applied AI capabilities to robotics, autonomous drones, and small on-device applications without network access, as well as the world's largest enterprise agentic workflows," Mistral wrote in the release. More on what that means in practice below. 

Multilingual and multimodal 

Mistral prides its latest family on two distinguishing factors: multilingual training and multimodal capabilities.

While models from US-based AI labs focus primarily on English training data, which can limit their applications for non-English developers, Mistral has historically created models trained on other languages. The company said Mistral 3 is especially primed for European languages. 

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Notably, the new suite of models stands out against headline-grabbing open-source models, such as Kimi K2 and those from DeepSeek, for being multimodal. While Kimi K2 is said to rival OpenAI's GPT-5, it's limited to text, making its use cases more narrow. 

"Usually, you have the best model in vision, the best model for text, while here, we actually squeezed everything into the same model," Guillaume Lample, Mistral co-founder and chief scientist, told in an interview. 

Mistral Large 3

Mistral Large 3, the biggest of the family at 675B parameters, is a Mixture of Experts (MoE) model, meaning it's separated into sub-networks, or "experts," that jointly address a query more efficiently than regular models. Specific experts will activate based on the content of the query, which lets the model handle bigger tasks without driving up astronomical compute. 

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With a 256k context window, Mistral Large 3 can handle complex queries ranging from document analysis and coding to creative content and more agentic use cases like workflow automation and assistant work. 

Ministral 3

The smaller subset of the Mistral 3 family includes several sizes: 14B, 8B, and 3B, and is split into three variants: Base (pre-trained), Instruct (optimized for taking directions in chat), and Reasoning. 

"The next wave of AI won't be defined by sheer scale, but by ubiquity -- by models small enough to run on a drone, in a car, in robots, on a phone or a computer laptop," Mistral said in the release, pointing out that small models are often preferable for real-life use cases. By keeping costs and latency down, they can be more accessible than heavier, slower, more expensive models that require more infrastructure to run on. 

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Mistral added that small models like Ministral 3 are also easier to customize, making them ideal for fine-tuning to enterprise workflows. The company emphasized that customization as the release's main appeal for developers across all kinds of projects. 

"By balancing efficiency with performance, Ministral 3 enables even resource-constrained environments to leverage cutting-edge AI without sacrificing capability or scalability," Mistral said. 

Edge AI and greater accessibility 

Available under an Apache 2.0 license, the entire Mistral 3 family is open-source; however, Mistral framed Ministral 3 specifically as accessible beyond that due to its portability. 

"Ministral 3 can be deployed on a single GPU, ranging from 16GB VRAM to just 4GB VRAM at a 4-bit quantization," the company wrote. "This eliminates the need for expensive, high-end hardware, making advanced AI accessible to startups, research labs, and enterprises of all sizes."

Mistral cited several use cases it designed the new, smaller models for, including "edge AI" applications, or situations where enterprises deploy AI to environments without Wifi. These include factory robots that use live sensor data to fix issues without relying on the cloud; drones used in natural disasters, search-and-rescue, or other emergencies that rely on vision and thermal data on-device; and smart cars equipped with AI assistants that can operate offline in remote areas. 

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That offline capability is especially important for getting AI models into the hands of people who wouldn't otherwise access them, according to Lample. 

"There are billions of people without internet access today, but they nonetheless have access to either a laptop, or they have a smartphone," he noted to . "They definitely have hardware on which they can run these small models. So it's actually something that could be kind of game-changing."  

Because edge AI applications are on-device, they also preserve data privacy for users, Mistral noted. 

"Open sourcing a broad set of models helps democratize scientific breakthroughs and brings the industry towards a new era of AI, which we call 'distributed intelligence,'" the company added in the announcement. 

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