Ministral 3 8B

Name: Ministral 3 8B
Price: 0.15 USD
Author: Mistral AI

Mistral AI · Ministral 3 · released 2025-12-02 · data checked 2026-11-01

Ministral 3 8B is a 8B-parameter dense model from Mistral AI (Europe), released 2025-12-02 under the Apache-2.0 license. It scores 55.2 on the Small LM Index, #7 of 26 small models. It needs about 6.7 GB of memory at 4-bit with an 8K-token context and costs $0.15 per million tokens through the cheapest API provider.

Small LM Index

55.2

#7 of 26 scored small models

Likely 50.5–59.7 · from 3 of 6 core benchmarks

Parameters

Dense

Price per 1M tokens

$0.15

$0.15 in / $0.15 out · blended 3:1

Context window

256K

tokens

Benchmarks

Each bar shows this model's score. The tick marks the best small model on that benchmark. Sources: 3 reported by Mistral AI. How we label sources.

GPQA Diamond Lab-reported Knowledge66.8

Class best: Qwen3.5 9B 81.7

AIME Lab-reported Math · AIME 2025 (Reasoning variant)78.7

Class best: Apriel 1.6 15B Thinker 88.0

LiveCodeBench Lab-reported Coding61.6

Class best: Granite 4.2 8B 73.2

Not reported: estimated

What the index model expects from this model's other scores. Not a measurement.

MMLU-Pro≈ 76

Humanity's Last Exam≈ 8

SWE-bench Verified≈ 38

Specifications

Lab: Mistral AI (FR · Europe)
Architecture: Dense transformer
Total parameters: 8B
Active parameters: All (dense)
Size class: Small
Context window: 256K tokens
Input modalities: text, image
Reasoning: Hybrid (thinking on/off)
License: Apache-2.0
Openness: Open weights
Memory to run: ~6.7 GB at 4-bit, 8K context (more sizes)
Hugging Face: 16.3K downloads (30 days) · 120 likes
API providers: 1 on OpenRouter
API price: $0.15 in / $0.15 out per 1M (OpenRouter, 2026-11-03)

Weights on Hugging Face ↗Try on OpenRouter ↗

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Run it locally

Uses mistralai/Ministral-3-8B-Reasoning-2512-GGUF (Q4_K_M). Swap the tag for another size: BF16, Q4_K_M, Q5_K_M, Q8_0.

Ollama

ollama run hf.co/mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M

llama.cpp

llama-server -hf mistralai/Ministral-3-8B-Reasoning-2512-GGUF:Q4_K_M

Then open http://localhost:8080 for a chat window.

vLLM (GPU server)

vllm serve mistralai/Ministral-3-8B-Reasoning-2512

Full-precision weights, for a server GPU.

Open in LM Studio

Memory needed: weights, context cache and runtime buffers.

Quantization	4K context	32K context	256K context
16-bit F16	17 GB	21 GB	52 GB
8-bit Q8_0	9.7 GB	14 GB	45 GB
4-bit Q4_K_M	6.1 GB	10 GB	41 GB

Check it against your GPU, Mac or phone →

Ministral 3 8B

Benchmarks

Not reported: estimated

Specifications

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Sources

Run it locally