👁 Mistral
Mistral
Mistral Small 3.2 24B
Current
701.1KDownloads595LikesJun 2025Released131K tokensContextApache 2.0License80 StrongQuality
Mistral Small 3.2 24B (24B parameters) requires approximately 18.9 GB of VRAM with Q4_K_M quantization. For the best balance of quality and speed, we recommend hardware with at least 22 GB of VRAM.
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— copy & paste to run locallyCopy-paste commands to run Mistral Small 3.2 24B on your machine.
Run
ollama run mistral-small3.2Quick specs
Parameters24B
Architecturevision
Context131K tokens
Modalitytext+vision
Min RAM9.4 GB
Rec. RAM14.6 GB (Q4_K_M)
LicenseApache 2.0
FamilyMistral Small
✓ Vision✓ Chat✓ Reasoning
About this model
- •Instruction following: Small-3.2 is better at following precise instructions
- •Repetition errors: Small-3.2 produces less infinite generations or repetitive answers
- •Function calling: Small-3.2's function calling template is more robust (see here and examples)
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Quantization options
VRAM estimates by quant level
No hardware detected — fit column shows raw VRAM estimates
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 9.4 GB | Low | — |
Q3_K_S | 3 | 11.8 GB | Low | — |
NVFP4 | 4 | 13.4 GB | Medium | — |
Q4_K_M | 4 | 14.6 GB | Medium | — |
Q5_K_M | 5 | 17.3 GB | High | — |
Q6_K | 6 | 19.7 GB | High | — |
Q8_0 | 8 | 25.7 GB | Very High | — |
F16 | 16 | 49.2 GB | Maximum | — |
Quality benchmarks
Mistral Small 3.2 24B benchmark scores
Coding
SWE-bench Verified—
HumanEval+92.9%
Aider Polyglot—
LiveCodeBench—
Reasoning
MMLU-Pro69.1%
GPQA Diamond46.1%
MATH-500—
ARC Challenge—
Source: official · 2025-06-21
Hardware compatibility
Fit estimates across all hardware
Computing compatibility...
Memory breakdown
Reference: RTX 2060 6GB
Weights14.6 GB
KV Cache2.4 GB
Runtime1.2 GB
Headroom0.6 GB
Frequently asked questions
FAQ — Mistral Small 3.2 24B
See also