Mistral Large 4 ships as a 1.05T-parameter open-weight multimodal model at $1.36 per million input tokens

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Mistral Large 4 ships as a 1.05T-parameter open-weight multimodal model at $1.36 per million input tokens

Mistral released Mistral Large 4, an open-weight multimodal model with 1.05T total parameters and 52B active parameters in a granular Mixture-of-Experts design, now in public preview.
API pricing is $1.36 per million input tokens, $0.14 per million cached input tokens, and $4.18 per million output tokens, with a 1M token context window.
Built-in capabilities include structured outputs, function calling, document QnA, batching, and agents with built-in tools.
Commenters say the weights on Hugging Face are due at the end of the month, and that the model costs about 10x DeepSeek on OpenRouter while trailing on coding.
The model is pitched mainly to EU companies that need European data residency under GDPR, since price and coding performance do not beat DeepSeek.

Hacker News opinions

I'd say the only real reason is EU compliance. It's pricier than DeepSeek on OpenRouter and the benchmarks I've seen put it behind.

Benchmarks don't tell the whole story though. Mistral is in the EU, France runs a lot of nuclear power, and GDPR is a real deciding factor for European companies. Even if it's weaker, I'm glad they're still in the fight.

Models are hard to reduce to one benchmark number, and some just feel better for specific use cases. Deepseek is the big shadow over every release because it's so cheap and good.

In a few years we might not need frontier models at all. Open-weight is only about half a year behind, and Gemini 2.0 Flash from 1.5 years ago couldn't even repeat a few paragraphs unchanged.

Wait, isn't this the same model from the previous discussion 15 days ago with 2044 points? What's the point of reposting it as a new story?

Some details and benchmarks were announced but the weights didn't get released yet, so that's probably why it's getting upvotes.

The visual intelligence looks better than DeepSeek 4.1 Flash, but coding isn't close to DS 4.1 1017. Weights are supposed to land on Hugging Face at the end of the month.

Weird thing is it keeps spitting out Chinese characters when I chat with it in German. Happened to me quite a lot the past few days.

What's more impressive to me is the training datacenter. Getting sub-SOTA results on a tenth of the resources of SOTA is the real story.

GDPR coverage is a big plus for me, and the training opt-out is very simple.

52B active parameters and my qwen-3.8-flash-next at home is supposedly competitive? Gotta test it to believe it.