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Magistral Small

Parameters

24B

Context Length

128K

Modality

Text

Architecture

Dense

KV Cache Rate

128 KB/tok

License

Apache 2.0

Release Date

10 Jun 2025

Knowledge Cutoff

Oct 2023

API Pricing (per 1M)

Input: $0.50 · Output: $1.50

Hardware Sizing

Precision

Context

Workload Memory: 14.68 GB

INT4 · 8K context

Base Weights: 11.18 GB

KV Cache: 1.00 GB

Runtime & Graphs: 2.50 GB

Minimum Required Hardware

1x NVIDIA L4 24GB

14.7 / 21.6 GB usable

2x RTX 4090 24GB

14.7 / 43.2 GB usable · TP 2

Apple M4 24GB

14.7 / 18.0 GB usable

Determine requirements for your specific workload:

Open in VRAM Calculator

Architecture Diagram

Input TokensToken EmbeddingPosition: AbsoluteHidden: 14.3k · Context: 128K · Vocab: 131.1kx 32 layersRMSNormPre-AttentionMulti-Head Attention32Q / 8KV headsHead dim: 128+RMSNormPre-FFNFeed-Forward NetworkSwiGLUIntermediate: 32.8k+Final RMSNormOutput Logits

Evaluation Benchmarks

Rank

#175

BenchmarkScoreRank

0.346

30

Graduate-Level QA

GPQA

0.682

88

0.15

139

Intelligence Index

Artificial Analysis

0.09

253

Rankings

Overall Rank

#175

Coding Rank

#130

About Magistral Small

Magistral Small is an open-source 24B multimodal reasoning model developed by Mistral AI, engineered for transparent, traceable multi-step logic. It supports multilingual reasoning across 24+ languages and native function calling for agentic AI.

Technical Specifications

Attention

Attention Structure

Multi-Head Attention

Attention Heads

32

Key-Value Heads

8

Attention Head Dimension

128

Position Embedding

Absolute Position Embedding

RoPE Theta

1,000,000,000

Normalization

RMS Normalization

Activation Function

SwigLU

Dimensions

Hidden Dimension Size

14,336

Number of Layers

32

FFN Intermediate Size (Dense)

32,768

Tokenizer

Vocabulary Size

131,072

About Magistral

Magistral is Mistral AI's first reasoning model series, purpose-built for transparent, step-by-step reasoning with native multilingual capabilities. Features chain-of-thought reasoning in the user's language with traceable thought processes. Excels in domain-specific problems requiring multi-step logic, from legal research and financial forecasting to software development and creative storytelling. Supports reasoning across numerous languages including English, French, Spanish, German, Italian, Arabic, Russian, and Chinese.


Other Magistral Models
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