Mistral AI Drops New Open-Source Model. The Internet Is Not Impressed, Except for One Thing

TL;DR

Mistral AI has released its new open-source model, Mistral Medium 3.5, featuring 128 billion parameters but has received a lukewarm response due to high costs and competition from cheaper Chinese models. Despite criticism, Mistral positions itself as a key player in the European AI landscape.

Key points

Mistral Medium 3.5 has 128 billion parameters.
It costs $1.50 per million input tokens and $7.50 per million output tokens.
Chinese models dominate the leaderboard, outperforming Mistral.
Mistral is the only major non-Chinese model in the open-source AI conversation.

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In brief

Mistral Medium 3.5 is a 128 billion parameter dense model priced at $1.50 input / $7.50 output per million tokens, far above comparable Chinese alternatives.
Chinese open-source models—Qwen, GLM, MiMo-V2—dominate the leaderboard top, leaving Mistral as a lonely Western holdout.
Mistral is positioning the release as a building block toward a future large flagship model. Mistral AI dropped Mistral Medium 3.5 on April 29. The Paris-based lab announced a dense 128-billion-parameter model, a set of agentic features—and walked straight into a wall of online “meh” reactions. The release came in three parts. First, the model itself. Second, remote coding agents via Mistral Vibe CLI—cloud-based coding sessions that can push pull requests to GitHub and run in parallel without you sitting at a terminal. Third, Work Mode in Le Chat, Mistral's ChatGPT-style consumer interface, which now handles multi-step autonomous tasks like email triage, research synthesis, and cross-tool workflows. Big ambitions, but a messy benchmark reality. Medium 3.5 scores 77.6% on SWE-Bench Verified—a coding benchmark that tests whether a model can fix real GitHub issues by generating working patches. It also hits 91.4% on τ³-Telecom, which measures agentic tool use in specialized environments. Mistral also merged three previously separate models (Medium 3.1, Magistral, and Devstral 2) into one set of weights with configurable reasoning effort per request.
Unified model replacing three is a real engineering win. The problem is what it costs and who it's up against. Mistral charges $1.50 per million input tokens and $7.50 per million output tokens. Alibaba's Qwen 3.6 at 27 billion parameters—less than a quarter of Medium 3.5's parameter count—scores 72.4% on the same SWE-Bench Verified benchmark and ships under Apache 2.0, meaning you can download and run it for free.

Did you know?

Parameters are what determine an AI's capacity to learn, reason, and store information. The more parameters, the wider the model's breadth of knowledge. Scroll through the open-source leaderboards and the picture is stark. The top spots belong to Alibaba’s Qwen, GLM from China's Zhipu AI, and MiMo-V2 from Xiaomi, all of them cheaper, more powerful and competitive than Mistral’s new release. Medium 3.5 hasn't even ranked on major independent leaderboards yet—third-party evaluations are still pending. The only good thing though, as some argue, is that Mistral is, at this point, the lone non-Chinese model with any serious presence in the open-source conversation.

Q&A

What are the key features of Mistral Medium 3.5?

Mistral Medium 3.5 is a 128 billion parameter model that includes remote coding agents and a consumer interface for multi-step tasks.

How does Mistral Medium 3.5 compare to Chinese models?

Mistral Medium 3.5 is significantly more expensive than Chinese models like Qwen and GLM, which offer comparable or better performance at lower costs.

What is the pricing structure for Mistral Medium 3.5?

Mistral charges $1.50 per million input tokens and $7.50 per million output tokens for the Medium 3.5 model.

Why is Mistral AI considered significant in the European AI market?

Mistral AI is seen as a major non-Chinese player in the European AI market, especially for enterprises needing compliance with GDPR and other regulations.