GLM 5.1 is an excellent model, but even at Q4 you're looking at ~400GB. Kimi K2.5 is really good too, and at Q4 quantization you're looking at almost ~600GB.

This model? You can run it at Q4 with 70GB of VRAM. This is approaching consumer level territory (you can get a Mac Studio with 128GB of RAM for ~3500 USD).

For the Claude-pilled people, I don't know if you only run Opus but when I was on the Pro plan Sonnet was already extremely capable. This beats the latest Sonnet while running locally, without anyone charging you extra for having HERMES.md in your repo, or locking you out of your account on a whim.

Mistral has never been competitive at the frontier, but maybe that is not what we need from them. Having Pareto models that get you 80% of the frontier at 20% of the cost/size sounds really good to me.

The different results on some benchmarks vibes as if this is truly an independently trained model, not just exfiltrated frontier logs, which I think is also really important - having different weight architectures inside a particular model seems like a benefit on its own when viewed from a global systems architecture perspective.

Note: I have more uncommitted speed improvements in my tree that I'll push soon, the current tree could be a little bit slower but not much, still super usable.

I don't understand one thing about Mistral, which I'm a fan being in Europe: they opened the open weights MoE show with Mixtral. Why are they now releasing dense models of significant sizes? In this way you don't compete in any credible space, nor local inference, nor remote inference since the model is far from SOTA and not cheap to serve. So why they are training such dense big models? Dense models have a place in the few tens of billion parameters, as Qwen 3.6 27B shows, but if you go 5 times that, it is no longer a fit, unless you are crushing with capabilities anything requiring the same VRAM, which is not the case.

Sometimes when a new release comes around from any provider I just want to test it a bit on the web. without paying and using an agent harness.

Why are they like this ;_;

Edit: Christ on a bike it's bad at drawing SVGs https://chat.mistral.ai/chat/23214adb-5530-4af9-bb47-90f5219...

Pre-agent, there wasn't always an obvious difference between models. Various models had their charms. Nowadays, I don't want to entertain anything less than the frontier models. The difference in capability is enormous and choosing anything less has a real cost in terms of productivity.

I've been a big fan of the smaller labs like Mistral and especially Cohere but it's been a while since I've been excited by a release by either company.

That said, I'm using mistral voxtral realtime daily – it's great.

That said, when I stop spending money on Gemini Ultra, I will give Mistral Vibe another 1-month test.

I like the entire business model and vibe of Mistral so much more than OpenAI/Anthropic/Google but I also have stuff to get done. I am curious if Mistral Vibe for $15/month is a stable business model (i.e., can they make a profit).

Their model listing API returns this:

  {
    "id": "mistral-medium-2508",
    "object": "model",
    "created": 1777479384,
    "owned_by": "mistralai",
    "capabilities": {
      "completion_chat": true,
      "function_calling": true,
      "reasoning": false,
      "completion_fim": false,
      "fine_tuning": true,
      "vision": true,
      "ocr": false,
      "classification": false,
      "moderation": false,
      "audio": false,
      "audio_transcription": false,
      "audio_transcription_realtime": false,
      "audio_speech": false
    },
    "name": "mistral-medium-2508",
    "description": "Update on Mistral Medium 3 with improved capabilities.",
    "max_context_length": 131072,
    "aliases": [
      "mistral-medium-latest",
      "mistral-medium",
      "mistral-vibe-cli-with-tools"
    ],
    "deprecation": null,
    "deprecation_replacement_model": null,
    "default_model_temperature": 0.3,
    "type": "base"
  },

But... that 1777479384 timestamp decodes to Wednesday, April 29, 2026 at 04:16:24 PM UTC

So is that the new Mistral Medium?

It's cool that they added comparisons to their own Mistral Small 4 119B A7B, which kind of shows that! They could have also included comparisons to something like Qwen Coder Next 80B A3B (or maybe the newer Qwen 3.6 35B A3B, or the 27B dense one), maybe DeepSeek V4 Flash 284B A13B, or the older GPT-OSS 120B A5B to illustrate that difference and where their model sits even better, it would probably give a more positive picture than just comparing themselves against a bunch of bigger models!

Come to think of it, alongside throwing some money at DeepSeek not just Anthropic, I probably should get a Mistral subscription as well sometime, to see how they perform on various tasks - cause they seem pretty cost effective and it's nice to support at least some EU orgs: https://mistral.ai/pricing

https://huggingface.co/mistralai/Mistral-Medium-3.5-128B

They more or less claim this exceeds Claude Sonnet 3.5 on most things, but is worse than Sonnet 3.6, and exceeds all other open models.

Oh and they have a cloud service that will code your apps "in the cloud". But, yeah, at this point, so does my cat.

And, yes, unsloth is on it: https://huggingface.co/unsloth/Mistral-Medium-3.5-128B-GGUF (but 4bit quant is 75G)

One thing in particular I was disappointed in was its bad explanations when asking about French grammar. It made multiple mistakes and the other models got it right, even Qwen 3.6 27b!

Anyway, I'm hoping they catch up some more.

Doesn't look to promising. Is there any reason to consider Mistral other than it's not US?