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Mistral Small 3 brings open source AI to the masses—smaller, faster, and cheaper

Jan 30, 2025 - venturebeat.com
Mistral AI, a European startup, has introduced a new language model, Mistral Small 3, which boasts 24 billion parameters and achieves 81% accuracy on standard benchmarks while processing 150 tokens per second. The model is designed to match the performance of models three times its size, such as Meta's Llama 3.3 70B, while significantly reducing computing costs. Mistral's approach emphasizes efficiency through improved training techniques rather than increased computing power, allowing the model to be trained on fewer tokens without reinforcement learning or synthetic data. This makes it particularly appealing for enterprises needing on-premises deployment for privacy and reliability, as it can run on a single GPU and handle most business use cases.

Mistral's strategy of focusing on smaller, more efficient models positions it as a potential leader in the AI industry, contrasting with the trend of developing larger, more expensive models. The company, valued at $6 billion and backed by Microsoft, is preparing for an IPO and aims to democratize access to advanced AI capabilities by making its models open source under the Apache 2.0 license. This approach could reshape the economics of AI deployment, making it more accessible and cost-effective for businesses. Mistral plans to release additional models with enhanced reasoning capabilities, further testing its efficiency-focused strategy against larger systems.

Key takeaways:

  • Mistral AI has unveiled a new language model, Mistral Small 3, which matches the performance of larger models while reducing computing costs.
  • The model focuses on efficiency through improved training techniques, achieving high performance without reinforcement learning or synthetic data.
  • Mistral Small 3 is designed for enterprises needing on-premises deployment for privacy and reliability, capable of running on a single GPU.
  • Mistral AI is positioning itself as a leader in the AI industry with a focus on smaller, efficient models and plans for an IPO.
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