Mistral Large 2
France's Closed-Frontier Flagship
Mistral's July 2024 flagship — a 123-billion-parameter dense model for the European market. Released under the Mistral Research License (free for research, paid for commercial). Followed by Mistral Medium 3 and Mistral Small 4 as Mistral built a tiered product line to compete with OpenAI's GPT-4o family.
Intelligence
Below avg
Speed
Slow
61 tok/s output
Cost
Moderate
$0.50 in / $1.50 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Established that a non-US, non-Chinese lab could ship a frontier model. Mistral Large 2 was Europe's first independent frontier LLM and the foundation of the EU's AI sovereignty argument.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Agent Workflows
Built for tool use and autonomous tasks.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Available
Product / App
Available
Open Source
Not released
Enterprise
Contact sales
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Mistral's most capable model at release — on par with GPT-4o, Claude 3 Opus, Llama 3 405B at 1/3 the parameter count
- 84.0% MMLU on the pretrained variant — set a new performance/cost Pareto point for open weights
- Single-node inference: 123B dense fits on one GPU server — no MoE-routing complexity
- Strong at function calling and parallel/sequential tool use — built for business app integration
- Particularly good at following precise instructions and multi-turn dialogue
- Open weights under Mistral Research License (commercial use needs paid agreement)
Best use cases
- Multilingual production apps — French/German/Spanish/Italian/Arabic/Chinese/Japanese first-class
- Function-calling agents and business integrations needing parallel tool calls
- 80+ programming languages including Python/Java/C++/JavaScript/Bash
- Self-hosted deployments where Llama 3.1 405B is too large but smaller models lose quality
Tools to try
Not ideal for
- Frontier reasoning leaderboards — newer Opus / GPT-5 / DeepSeek R1 lead by significant margins
- Free-to-use commercial deployments (research license requires payment for production)
- Vision / multimodal tasks — text only at this generation
Model Evolution
Mistral Large 2
Jul 2024
Pixtral Large
Nov 2024
Codestral 25.01
Jan 2025
Mistral Large 3 (v25.12)
Dec 2025