Mistral Small 4
By Mistral AI · France
Updated 2026-07-13
Overview
Mistral AI's 2026 flagship MoE with 119B total and 6.5B active parameters, unifying chat, reasoning, vision, and code in a single Apache 2.0 model.
When to pick this model
- Consolidating multiple Mistral deployments into one model
- Vision plus reasoning workloads on a prosumer rig
- Long-context analysis up to 256K tokens
- European-data-sovereignty deployments
- Apache-licensed commercial products
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 72 GB |
| Q5_K_M | 86 GB |
| Q8_0 | 128 GB |
| FP16 (no quantization) | 238 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Mistral Small 4 is server-class even at Q4_K_M (72 GB). Stepping up to Q8_0 nearly doubles the footprint to 128 GB, and unquantized FP16 weights take 238 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Mistral Small 4 needs roughly 96 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 3 tokens/sec on entry-level GPUs, on the order of 12 tokens/sec on a mid-range card, and up to 30 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Mistral Small 4 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Mistral Small 4 |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 72 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 72 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 72 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 72 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 72 GB at Q4_K_M |
Which GPU should you buy to run Mistral Small 4?
To run Mistral Small 4 locally at Q4, you need ~72 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Strengths
- Unifies chat, reasoning, vision, and code in one model
- Only 6.5B active parameters for fast inference
- 256K context window
- Apache 2.0 license
- European lab with strong French and EU-language support
Limitations
- 72GB+ in Q4 requires a prosumer multi-GPU setup
- Breaks continuity with the Small 3.x line
- Newer release means thinner ecosystem
Typical workloads
In our catalog grid, Mistral Small 4 is filed under Agents, Vision, Code, Reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks; vision-language work — screenshots, charts, scanned documents; multilingual workloads; French-language output where quality matters.
The 250k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE 119B/6.5B active · 256k ctx · unifies instruct+reasoning+vision+code
Training: Replaces Small 3.x and Pixtral in a single model.
Mistral's most ambitious open release yet, ideal if you want one model covering four product lines.
Quick start
# HuggingFace : mistralai/Mistral-Small-4 (pas encore de tag Ollama officiel)Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Mistral Small 4 need?
At the recommended Q4_K_M quantization, Mistral Small 4 needs about 72 GB of VRAM. Q8_0 takes 128 GB, and unquantized FP16 weights take 238 GB.
Can Mistral Small 4 run without a GPU?
Yes — with roughly 96 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Mistral Small 4 support?
Mistral Small 4 supports a 250k-token context window (256,000 tokens).
Can I use Mistral Small 4 commercially?
Yes. Mistral Small 4 is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is Mistral Small 4 on consumer hardware?
Our compatibility engine estimates on the order of 12 tokens/sec on a mid-range GPU and up to 30 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Mistral Small 4 should I download first?
Start with Q4_K_M (72 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.