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Mixtral 8x22B

Mixtral 8x22B

8x22B v0.1 · Family: Mistral
Mistral AI’s open-source (Apache 2.0) SMoE model: 141B parameters (39B active), 64K context, native function calling, strong at math and code.
✓ Active✓ Public access⚖ Open sourceLLMTool-using model📁 Mistral
Context window
64K
tokens
Parameters
141B total / 39B active
parameters
Release date
17 April 2024
Access:DownloadAPIHostedDeployment:💻 Local☁ Cloud

Overview

Mixtral 8x22B is an open-source Sparse Mixture-of-Experts (SMoE) language model developed by Mistral AI and released on 17 April 2024. It has 141 billion total parameters, of which only 39 billion are activated per token, delivering much faster inference than dense models of comparable quality.

The model supports a 64K-token context window and is natively multilingual — fluent in English, French, Italian, German and Spanish. It stands out for strong mathematics and coding abilities, as well as native function calling support.

Mixtral 8x22B was released under the Apache 2.0 license — one of the most permissive open-source licenses — with downloadable weights and access via la Plateforme (Mistral AI’s console). It can therefore be run both locally and in the cloud.

In benchmarks, the instructed version achieves 90.8% on GSM8K (8-shot) and 44.6% on MATH (maj@4). The model outperforms LLaMA 2 70B on multilingual benchmarks (French, German, Spanish, Italian) and achieves top results on coding tasks (HumanEval, MBPP).

Classification
LLMTool-using model
Family: Mistral
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 useFine-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