Mistral Medium 3.5

The one thing I would want everyone curious about local LLMs to know is that being able to run a model and being able to run a model fast are two very different thresholds. You can get these models to run on a 128GB Mac, but we need to first tell if Q4 retains enough quality (models have different sensitivities to quantization) and how fast it runs.

For running async work and background tasks the prompt processing and token generation speeds matter less, but a lot of Mac Studio buyers have discovered the hard way that it's not going to be as responsive as working with a model hosted in the cloud on proper hardware.

For most people without hard requirements for on-site processing, the best use case for this model would be going through one of the OpenRouter hosted providers for it and paying by token.

> This beats the latest Sonnet while running locally

Almost every open weight model launch this year has come with claims that it matches or exceeds Sonnet. I've been trying a lot of them and I have yet to see it in practice, even when the benchmarks show a clear lead.

Sad to see all the non Chinese open source models being at least one generation behind.

Before February I was able to use Opus on High exclusively on my Max plan no problem. Now I've shifted to just using Sonnet on high and yeah, its pretty capable. I love that, Claude Pilled. ;)

[1]: There is no other common benchmark in the blog.

Not sure it will beat Sonet at Q4.

>This is approaching consumer level territory (you can get a Mac Studio with 128GB of RAM for ~3500 USD).

For $3500 I can get 7-8 years of GLM using coding plans, have a faster model and much better code quality.

In the US with our broken system of capitalism, it’s the only way we can tether these companies to reality. Left to their own devices, I’m not convinced they would actually compete with each other on price.

Buy nobody like to talk about how “moat” building is fundamentally anti-competitive, even in name.

Funny that self proclaimed capitalists hate the system in practice. Commodity pricing is what truly terrifies them.

https://chatgpt.com/share/69f239e8-7414-83a8-8fdd-6308906e5f...

Tldr: qwen3.6-27b, a 4.7x smaller model, have similar performance.

On the bike would be an improvement. Geez.

I know SVGs may not be the best benchmark, but that matches my experience of trying to run a (previous) Mistral model in Mistral Vibe, asking it to help me configure an MCP server in Vibe. It confidently explained that MCP is the MineCraft Protocol and then began a search of my computer looking for Minecraft binaries.

They were perhaps right.

  curl https://api.mistral.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $(llm keys get mistral)" \
  -d '{
    "model": "mistral-medium-3.5",
    "messages": [
      {"role": "user", "content": "Generate an SVG of a pelican riding a bicycle"}
    ]
  }'

Weird that it doesn't show up in the model list:

  curl https://api.mistral.ai/v1/models \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $(llm keys get mistral)" | jq

It's just apples to oranges.

There is not a clear, across the board, winner on non-agentic tasks between Gemini, ChatGPT, and Claude - the simple chatbot interface.

But Claude Code is substantially better than Codex which itself is notably better than Gemini-cli.

In this vein, it should not be surprising that Claude Code is way better than non-frontier models for agentic coding... It's substantially better than other frontier models at specialized agentic tasks.

This is a very naive and misguided opinion. In most tasks, including complex coding tasks, you can hardly tell the difference between a frontier model and something like GPT4.1. You need to really focus on areas such as context window, tool calling and specific aspects of reasoning steps to start noticing differences. To make matters worse, frontier models are taking a brute force approach to results which ends up making them far more expensive to run, both in terms of what shows up on your invoice and how much more you have to wait to get any resemblance of output.

And I won't even go into the topic or local models.

The advantage to a dense model like this Mistral one is that it is as smart as a much larger MoE model so it can fit on less GPUs. The tradeoff is that it is much slower since it has to read 100% of its weights for every token, MoE models typically only read about a tenth (though sparsity levels vary).

The only benefit of leading is mindshare. OpenAI is doubling down on that, by investing in communication companies. That's their pathetic attempt at a "moat".

There is no way it exceeds “all other” open models - but it does exceed all of Mistral’s past models.

You can see it getting blown past by GLM 5.1 and Kimi in this.

Still excited to give it a try

Difficult to say, this information is not really public. That said, those investors include EU agencies and European multinational companies and governments. It’s not as flashy as the ridiculous sums OpenAI is getting but it should be enough to keep them going for a while.

They also have a different business model. They are selling their expertise to fine tune and adapt their models to on-premises computers (which they can help you build) to handle confidential data and information. I would not be surprised that the revenue they get from normal people is negligible in comparison.

I have been using DeepSeek and GLMnmodels with OpenCode and Codex and Claudr side by side.

I have not found the Chinese models lacking. I enjoy for coding and like to maintain full control of my codebade and deeply care about the GOF patterns. So I am very stringent in terms of what I want the LLM to code and how to code.

So from my perspective, they are all about the same.