How the French startup is challenging the US giants with its latest model
The AI world has shaken up quite a bit in recent weeks. While everyone was staring at GPT-5, Gemini 3 and Claude Opus 4.5, French startup Mistral AI announced at the end of November 2025 Mistral 3 A real bang landed. And yes, this is actually Europe's only serious answer to US dominance in large language models.
What is Mistral 3?
Mistral 3 is not just a model, it is a complete model family. The flagship, Mistral Large 3, is a monster with 675 billion parameters (of which 41 billion are active at the same time). In addition, they MinistralModels with 3B, 8B and 14B parameters that are small enough to run on your laptop.
What's special? Everything is open-source under Apache 2.0 license. You can download the models, customize them, use them commercially. So you have no restrictions. This is a clear contrast to OpenAI, Google and Anthropic, which hide their models behind APIs.
The performance check: How does Mistral 3 work?
Against open source competition
Mistral Large 3 is at the top of the open source league. On the LMArena Leaderboard, it ranks second among non-reasoning open source models (ranked 6th overall). The model performed at eye level with the best open models such as Meta’s Llama 4 Maverick.
Mistral Medium 3, a newer mid-range model, reaches about 90% The performance of Claude Sonnet 3.7, at 8 times lower cost. This makes it less expensive than DeepSeek V3 and more performant than Cohere Command A.
Against the closed US models
Here's where it gets interesting: Mistral Large 3 is slightly behind the absolute top models in standard benchmarks. In direct comparisons:
- ChatGPT (GPT-4o): Still has a slight head start on complex reasoning tasks. OpenAI’s latest models, such as GPT-5.1, are still above that.
- Claude Sonnet 3.7 / Opus 4.5: Claude remains strong in creative writing and nuanced understanding of the text. Anthropic’s latest models lead particularly in complex agentic tasks.
- Gemini 2.5 Pro: Google’s advantage lies in the gigantic context window (over 1 million tokens vs. Mistral’s 256K) and in the integration of multimodal data.
- DeepSeek V3: The Chinese surprise shit shows stronger performance in pure coding and math tasks. DeepSeek was trained with only 2.8 million GPU hours, which is incredibly efficient.
Where Mistral shines
Mistral has some real strengths:
multilingualism: While many US models are primarily optimized for English, Mistral masters French, German, Spanish and Italian with cultural understanding. This is not a small thing for European companies. – Apertus from Switzerland also takes a similar approach.
Coding and STEM: Mistral Large 3 shows impressive results in programming tasks and mathematical problems. In tests, coding tasks were partially ahead of Claude and GPT-4o.
Value for money: The Ministral models deliver amazing results with minimal resource requirements. The 8B model runs on a single RTX 4090, making it perfect for local deployments.
Edge computing: The small Ministral models are optimized for drones, robots and IoT devices. This is a niche that the major US providers hardly serve.
The map of Europe
CEO Arthur Mensch (formerly DeepMind) deliberately positions Mistral as a European alternative. This means:
- GDPR compliance From the beginning
- Data sovereignty because you can host everything yourself
- transparency through open source
- independence by US tech giants
For European companies that don't want to send their data to California, this is a real selling point. In particular, authorities and regulated industries are taking a close look.
The brutal truth
Let's be honest: Mistral Large 3 still Not at the absolute peak level of GPT-5.1 or Opus 4.5. With the toughest reasoning benchmarks and complex agentic workflows, proprietary systems are ahead of the pack.
But (and this is a big “but”) the gap is closing quickly. Mistral Large 3 was trained on only 3,000 H200 GPUs and costs a fraction of the US models. The efficiency is impressive.
As Guillaume Lample (Mistral’s Chief Scientist) puts it: “Compared to closed models, it is a bit behind. But we are playing a long-term strategic game.”
Who is Mistral 3 the right choice for?
Perfect if you...
- Need full control of your model
- Pay attention to costs (high-volume applications)
- Plan local/on-premise deployments
- Data sovereignty is important
- Working with European languages
- Edge Computing Scenarios
Not ideal if...
- You absolutely need cutting-edge reasoning
- Huge context windows needed (>256K)
- Developing Agentic AI with Complex Multi-Step Workflows
The conclusion
Mistral 3 is not a ‘GPT killer’, but it does not have to be. It is Europe's strongest response to US dominance and shows that open source models can get damn close to proprietary systems.
The true innovation lies not only in performance, but in the approach: Distributed intelligence instead of cloud monopoly. Adaptability instead of vendor lock-in. Transparency instead of black box.
For many companies, Mistral 3 will be the better choice, not because it gets the absolute maximum, but because it offers the best overall package of performance, cost, control and privacy.
And let's be honest: A French startup that competes against OpenAI, Google and Anthropic and can actually keep up? That alone deserves respect.
Interested? Mistral 3 you can Test via the Mistral API or directly Download Hugging Face. You can also directly on the website Let's go prompt. The Ministral models even run on Ollama for local experiments and are of course also suitable for LM Studio available.
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