Mistral Large 3
A solid pick for self-hosting and full data control.
Overview
Mistral Large 3 is THE reference model for sovereign European use: France-hosted, full GDPR compliance, French team. French quality is excellent (Claude-level). Solid coding and reasoning performance, slightly behind the priciest US frontiers but at a much lower price.
Skill profile
Three independent measures
What people prefer, what the model can do, what the market uses. Each source has its biases and none is enough on its own: we show them side by side, never blended.
What do these scores mean?
PhD-level science questions (physics, chemistry, biology), with no tool access.
Resolving real GitHub issues under real conditions (SWE-bench Verified).
Competition-level math problems.
General knowledge across dozens of academic subjects.
Python code generation from specifications.
Strengths
- Excellent at code
- Strong French quality
- World-class on Arena
- Has a free tier
Who is it for
- you want to control cost or self-host
- you work in French
- your data must stay in the EU
Ideal use cases
- GDPR-compliant European apps
- European multilingual
- French apps
- EU enterprise
Access & availability
Key specifications
What it costs per month
Estimate from sourced API prices and a hardware tier for self-hosting. Assumptions are editable.
Assumptions: 36-month amortisation, 0.25 €/kWh, 22 days/month, 1 $ = 0.92 €. Excludes labour, software licences, redundancy.
nAIvigate Trust Index
What the vendor offers and documents for professional use in Europe. An undocumented criterion counts as zero and stays visible: the score rises when the vendor publishes, not when we assume.
19/19 criteria documented · 100 % · 12 of them set by rule
See the criteria▾
Trust Index badge
Show this score in your docs, README or security page. It updates automatically.
[](https://naivigate.com/en/model/mistral-large-3)Data & compliance
What we know, line by line, with source and date. These lines feed the Data and Enterprise families of the Trust Index above. GDPR compliance depends on your processing, not on the model alone.
| EU data residency | Yes |
| Data processing agreement (DPA) | N/A· self-hosted: data never leaves |
| Transfer outside the EU | No |
| Training on your data | No |
| Configurable retention | N/A· self-hosted: data never leaves |
| Zero Data Retention | N/A· self-hosted: data never leaves |
| Self-hostable | Yes |
| Declared hosting | EU |
| Anonymisable data | Yes |
Quality of this card’s data
This score measures the quality of the data on this card: price, context, Elo, licence, documented, sourced and fresh. It measures neither the model nor its compliance: for that, see the Trust Index above.
8/8 documented · 4/8 primary sources
Run it locally
| Quantization | Disk | RAM / VRAM | Typical hardware |
|---|---|---|---|
| Q4 · recommended | 384.7 GB | 425 GB | Server GPU infra (H100/A100…) |
| Q8 · balanced | 722.2 GB | 796 GB | Server GPU infra (H100/A100…) |
| FP16 · max quality | 1350 GB | 1487 GB | Server GPU infra (H100/A100…) |
Estimates for a moderate context. Long contexts need more RAM (KV cache).
Deploy
Copy-ready commands generated from this card. Adjust context length and GPU count to your hardware.
OpenAI-compatible server for production on NVIDIA GPUs.
pip install vllm
vllm serve mistralai/Mistral-Large-3-675B-Instruct-2512 \
--max-model-len 32768 \
--tensor-parallel-size 2 \
--dtype autoAdvanced data · for expertsArchitecture, modalities, detailed cost, full benchmarks▾
| Arena Elo | 1414 |
| MMLU | 84.5% |
| GPQA | 64.0% |
| HumanEval | 88.0% |
| SWE-Bench | 50.0% |
| MATH | 84.0% |