31–40 of 243 posts
Extremely cool! I just wish they would also include comparisons to SOTA models from OpenAI, Google, and Anthropic in the press release, so it's easier to know how it fares in the grand scheme of things.
The lack of the comparison (which absolutely was done), tells you exactly what you need to know.
- Mistral Large 3 is comparable with the previous Deepseek release.
- Ministral 3 LLMs are comparable with older open LLMs of similar sizes.
- Gemini 3.0 Pro : 84.8
- DeepSeek 3.2 : 83.6
- GPT-5.1 : 69.2
- Claude Opus 4.5 : 67.4
- Kimi-K2 (1.2T) : 42.0
- Mistral Large 3 (675B) : 41.9
- Deepseek-3.1 (670B) : 39.7
The 14B 8B & 3B models are SOTA though, and do not have chinese censorship like Qwen3.
Extremely cool! I just wish they would also include comparisons to SOTA models from OpenAI, Google, and Anthropic in the press release, so it's easier to know how it fares in the grand scheme of things.
This is big. The first really big open weights model that understands images.
How is this different from Llama 3.2 "vision capabilities"? https://www.llama.com/docs/how-to-guides/vision-capabilities...
Earlier quoted context omitted.
The lack of the comparison (which absolutely was done), tells you exactly what you need to know.
Here's what I understood from the blog post: - Mistral Large 3 is comparable with the previous Deepseek release. - Ministral 3 LLMs are comparable with older open LLMs of similar sizes.
I'm not sure how these new models compare to the biggest and baddest models, but if price, speed, and reliability are a concern for your use cases I cannot recommend Mistral enough.
Very excited to try out these new models! To be fair, mistral-3-medium-0525 still occasionally produces gibberish ~0.1% of my use cases (vs gpt-5's 15% failure rate). Will report back if that goes up or down with these new models
Mistral had the best small models on consumer GPUs for a while, hopefully Ministral 14B lives up to their benchmarks.