Model
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Mistral Small 3.2 (24B)

OPEN
Mistral AI · Mistral Small family · released Jun 20, 2025

Drop-in upgrade over 3.1: markedly better instruction following, roughly half the infinite-generation errors, more reliable function calling.

ReasoningCodingVisionFunction callingTool useAgentic
1011.7
Elo · rank #222
Parameters
24B
Active params
24B (dense)
Context
128K tokens
Architecture
Dense transformer (24B), vision-language, incremental update of Small 3.1
License
Apache 2.0
Languages
API price (in/out)
$0.08 / $0.2
Modalities
text · vision
Benchmark results
Bar shows position within the tracked field; marker = field best
AI2DVision92.9%#15
best: Molmo 72B · 96.3%
Arena-Hard v2Human preference43.1%#8
best: Qwen3-Max · 86.1%
ChartQAVision87.4%#12
best: MiniMax-VL-01 · 91.7%
DocVQAVision94.9%#9
best: Qwen2-VL-72B · 96.5%
GPQA DiamondReasoning46.1%#209
best: GPT-6 Astra · 96.0%
HumanEval+Coding92.9%#1
best: this model · 92.9%
MATH-500Math69.4%#93
best: GPT-5 · 99.4%
MathVistaVision67.1%#35
best: Seed 2.1 Pro · 90.7%
MBPP+Coding78.3%#7
best: Llama 3.1 405B · 88.6%
MMLU-ProKnowledge69.1%#94
best: Claude Fable 5 · 91.5%
MMLUKnowledge80.5%#86
best: OpenAI o3 · 92.9%
MMMUVision62.5%#72
best: Claude Fable 5 · 89.3%
SimpleQAKnowledge12.1%#33
best: GPT-4.5 · 62.5%
WildBenchHuman preference65.3%#1
best: this model · 65.3%
Run it locally
VRAM @ Q4
14.5 GB
VRAM @ FP16
55 GB
Fits on (Q4)
RTX 4070 Ti 16GBRTX 3090 24GBRTX 4090 24GBRTX 5090 32GBM4 Pro 48GBM3 Max 128GBM3 Ultra 512GBA100 80GBH100 80GBH200 141GBB200 192GB
~70 tok/s on RTX 4090 (Q4, llama.cpp)
Quantizations
GGUF Q4 · AWQ · MLX
Fine-tune it
Permissive
QLoRA17.1 GB1× RTX 3090 24GB
LoRA51.9 GB1× A100 80GB
Full fine-tune386.0 GB4× H200 141GB
QLoRA SFT on ~10k samples ≈ $12.36 (1× RTX 3090 24GB)
API price $0.08/$0.2 · each benchmark row carries its own source badge (see methodology)