Mistral Small 3vsQwen 2.5 32B
Complete comparison between Mistral Small 3 (24B parameters, Mistral AI) and Qwen 2.5 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 3 | Qwen 2.5 32B |
|---|---|---|
| Settings | 24B | 32B |
| Family | Mistral | Qwen |
| Author | Mistral AI | Alibaba |
| Origin | FR | CN |
| License | Apache 2.0 | Apache 2.0 |
| Context | 32 768 tokens | 131,072 tokens |
| Output | January 2025 | September 2024 |
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 3 | Qwen 2.5 32B |
|---|---|---|
| Q4_K_M (lightweight) | 14 GB | 19 GB |
| Q5_K_M (balanced) | 17 GB | 23 GB |
| Q8 (near-lossless) | 26 GB | 35 GB |
| FP16 (max quality) | 48 GB | 64 GB |
| CPU-only RAM | 24 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 3 | Qwen 2.5 32B |
|---|---|---|
| RTX 4090 (24 GB) | 40 tok/s · Q5_K_M | 30 tok/s · Q5_K_M |
| RTX 4080 (16 GB) | 15 tok/s · Q4_K_M | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 15 tok/s · Q8 | 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 3
Excellent quality-to-size ratio at release (early 2025). Competes with 70B models.
- Quality close to Llama 70B
- Low latency
- 128k context
- Excellent for agents and tool use
- Requires at least 16 GB of VRAM in Q4
- Worse at coding than Qwen Coder
Installation
Qwen 2.5 32B
Rivals 70B models on most benchmarks.
- Open-weight 32B reference model in 2024
- 128k context
- Apache 2.0
- Very strong at math, coding, and reasoning
- Requires 19 GB of VRAM in Q4
- Outperformed by Qwen 3 32B in 2025
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Mistral Small 3 or Qwen 2.5 32B?
On an RTX 4090, Mistral Small 3 runs at Q5_K_M (~40 tok/s), Qwen 2.5 32B in Q5_K_M (~30 tok/s). In pure throughput, Mistral Small 3 wins. See the configurator to test your exact GPU.
Mistral Small 3 or Qwen 2.5 32B: which is better in French?
For formal French (writing, summarization, translation), Mistral Small 3 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, Mistral Small 3 fits in 14 GB versus 19 GB for its rival—a 5 GB difference, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Mistral Small 3 or Qwen 2.5 32B for coding?
Mistral Small 3 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 3 is under Apache 2.0 — free for commercial use. Qwen 2.5 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: Mistral Small 3 (24B). More capable: Qwen 2.5 32B (32B). 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.