Mistral AI’s open-source (Apache 2.0) SMoE model: 141B parameters (39B active), 64K context, native function calling, strong at math and code.
Context window
64K
tokens
Parameters
141B total / 39B active
parameters
Release date
17 April 2024
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud
Overview
Applications
Access & deployment
DownloadAPIHosted
LocalCloud
Weights: Open source
Key parameters
📏 Context: 64K
🧩 Parameters: 141B total / 39B active
✓ Tools · ✓ Fine-tuning
📥 Input: text
Technical specification
Context window
64K
tokens
Parameters
141B total / 39B active
parameters
License
Apache 2.0
Hardware requirements
An open-weights model (Apache 2.0) available for download; its 141B total parameters require multiple large-VRAM GPUs for local inference (the sparse activation of 39B speeds up runtime). Also available via Mistral AI’s la Plateforme in the cloud.
Features:✓ Tool use✓ Fine-tuning
Modalities
⬇ Input
text
⬆ Output
textcode
Capabilities and applications
Native model capabilities
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning — equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Multilingual
Competence in many natural languages (from a few to over a hundred): understanding, generation, translation, and code-switching within a single conversation. Frontier models support a wide range of languages with comparable quality.
Category: language
Long context
Support for large context windows — tens to hundreds of thousands (or millions) of input tokens. Enables analysis of entire codebases, long documents, and many parallel conversations without losing earlier information. GPT-5.1 supports 400,000 tokens.
Category: language
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning
Function Calling
Category: planning
Instruction following
Precisely following instructions contained in the prompt: response format, length, style, constraints (e.g. 'reply in six words'). GPT-5.1 significantly improved this capability compared to GPT-5.
Category: language
Benchmark results
2 benchmarks
GSM8K
accuracy · 8-shot (instructed version)
90.8%
📄 Mistral AI — Mixtral 8x22B (mistral.ai/news/mixtral-8x22b)
MATH
maj@4 · instructed version
44.6%
📄 Mistral AI — Mixtral 8x22B (mistral.ai/news/mixtral-8x22b)
Technical architecture
Core Architecture
Training Techniques
