Mistral Small 4vsQwen 3 32B
Complete comparison between Mistral Small 4 (119B parameters, Mistral AI) and Qwen 3 32B (32B, Alibaba). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data — no copy-pasting between pages.
In brief
| Feature | Mistral Small 4 | Qwen 3 32B |
|---|---|---|
| Settings | 119B | 32B |
| Family | Mistral | Qwen |
| Author | Mistral AI | Alibaba |
| Origin | FR | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 256,000 tokens | 131,072 tokens |
| Output | March 2026 | April 2025 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Mistral Small 4 | Qwen 3 32B |
|---|---|---|
| Q4_K_M (lightweight) | 72 GB | 19 GB |
| Q5_K_M (balanced) | 86 GB | 23 GB |
| Q8 (near-lossless) | 128 GB | 35 GB |
| FP16 (max quality) | 238 GB | 64 GB |
| CPU-only RAM | 96 GB | 32 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Mistral Small 4 | Qwen 3 32B |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | 30 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | ✗ too heavy | 12 tok/s · Q8 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Mistral Small 4
119B/6.5B active MoE unifies chat + reasoning + vision + code. The French flagship of 2026.
- Flagship French MoE
- Unifies 4 Mistral products
- 256k ctx
- Apache 2.0
- 72+ GB in Q4 — prosumer workstation
- Breaks Small 3.x continuity
Installation
Qwen 3 32B
Dense 32B with thinking mode. MMLU-Pro 65.5, SuperGPQA 39.8.
- Solid reasoning in thinking mode
- 131k ctx
- Apache 2.0
- Worse than QwQ-32B dedicated to pure reasoning
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Mistral Small 4 or Qwen 3 32B?
On a RTX 4090, Mistral Small 4 doesn't fit in 24 GB — part of it must be offloaded to the CPU, Qwen 3 32B in Q5_K_M (~30 tok/s). See the configurator to test your exact GPU.
Mistral Small 4 or Qwen 3 32B: which is better in French?
For formal French (writing, summarization, translation), Mistral Small 4 has the edge—trained on a dense French-language corpus. The other remains usable, but for less demanding use cases.
Which uses the least VRAM?
In Q4_K_M, Qwen 3 32B fits in 19 GB versus 72 GB for its rival — a 53 GB gap, significant if you're targeting an RTX 3060 or an 8 GB 4060 Ti.
Mistral Small 4 or Qwen 3 32B for coding?
Mistral Small 4 is the best fit (code-specialized, top HumanEval scores). For an IDE copilot, pair it with Continue or Aider.
Can these models be used in commercial production?
Mistral Small 4 is under Apache 2.0 — free for commercial use. Qwen 3 32B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 3 32B (32B). More capable: Mistral Small 4 (119B). If you're unsure, run the configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.