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Mistral 3 family of models released (mistral.ai)

826 points by pember · 234 days ago · 236 comments on HN

Article summary

Mistral AI has released the Mistral 3 family of models, including three small, dense models and a large, sparse mixture-of-experts model. The models are open-sourced under the Apache 2.0 license and are designed for various use cases, including edge and local applications. The Mistral 3 models are claimed to offer a good performance-to-cost ratio and are available on several platforms. The release includes base and instruction-fine-tuned versions of the models, with a reasoning version coming soon.

Main themes

  • AI model releases
  • Open-source models
  • Performance comparison
  • Edge computing
  • Multimodal models
  • Customization options

What commenters say

  • The lack of comparison to SOTA models from OpenAI, Google, and Anthropic in the press release implies that Mistral 3 may not be competitive with these models.
  • Comparing Mistral 3 to closed-source SOTA models is unfair, as they are in a different weight class and target different users.
  • Mistral 3's focus on open-source and local hosting options makes it a viable choice for companies that prioritize data security and compliance.
  • The performance difference between Mistral 3 and closed-source SOTA models is relatively small, with some commenters suggesting that this could 'pop the bubble' of overhyping AI model performance.
  • Some commenters believe that Mistral 3's release is not competitive with other open-source models, such as Deepseek, and that it may not be a significant improvement.
  • The use of open-source models like Mistral 3 can be seen as a step in the right direction towards more transparency and accountability in AI development.
  • The ranking of Mistral Large 3 on the LMArena leaderboard, behind other major SOTA models, suggests that it may not be the top-performing model, but the difference in performance may be relatively small.
  • Some commenters argue that the comparison of AI models should focus on their suitability for specific use cases and applications, rather than just their raw performance metrics.