Mistral Small 4 vs Qwen 3 32B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Mistral Small 4 | Qwen 3 32B |
|---|---|---|
| Parameters | 119B | 32B |
| Author | Mistral AI | Alibaba |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 72 GB | 19 GB |
| VRAM at Q5 | 86 GB | 23 GB |
| VRAM at Q8 | 128 GB | 35 GB |
| VRAM at FP16 | 238 GB | 64 GB |
| Use cases | chat, general, code, vision, reasoning, multilingual, fr, moe | chat, general, reasoning, multilingual |
Verdict
Mistral Small 4 is significantly larger (119B vs 32B), so expect higher quality but heavier VRAM and slower throughput.
The two models at a glance
About Mistral Small 4
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. 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.
About Qwen 3 32B
Alibaba's 32B dense flagship with thinking mode, scoring 65.5 on MMLU-Pro and 39.8 on SuperGPQA. The strongest general-purpose Qwen 3 dense model before stepping up to the MoE. Strengths: Strong reasoning with thinking mode enabled, Solid MMLU-Pro and SuperGPQA scores for its size, 131K context window, Apache 2.0 license.
How they compare
Mistral Small 4 comes from Mistral AI and Qwen 3 32B from Alibaba, they belong to the Mistral and Qwen families respectively. This comparison is built entirely from structured specs — parameter count, VRAM by quantization, context window, license, and published benchmark scores — so the verdict below reflects measurable differences rather than marketing claims.
At 119B vs 32B parameters, Mistral Small 4 is the larger of the two. At Q4, Qwen 3 32B fits in about 19 GB of VRAM versus 72 GB for the other — a 53 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Mistral Small 4 is tuned for chat, general, code, vision, reasoning, multilingual, fr, moe, while Qwen 3 32B leans toward chat, general, reasoning, multilingual. Match the model to your dominant workload rather than to raw size.
The smaller model, Qwen 3 32B, will generally generate tokens faster on the same hardware. For long-context work, Mistral Small 4 offers the bigger window (250k vs 128k tokens).
Memory, quantization & throughput
Across quantization levels, Mistral Small 4 requires Q4 ≈ 72 GB, Q5 ≈ 86 GB, Q8 ≈ 128 GB, FP16 ≈ 238 GB, while Qwen 3 32B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB. In practice Mistral Small 4 spills past 24 GB even at Q4, so plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity of Q8 or FP16.
Without a GPU, Mistral Small 4 needs roughly 96 GB of system RAM to run on CPU and Qwen 3 32B about 32 GB — workable for offline use but far slower than GPU inference. On a mid-range GPU you can expect on the order of 12 tokens/sec from Mistral Small 4 and 12 from Qwen 3 32B, scaling up to 30 and 30 tokens/sec on high-end hardware.
Which fits your GPU
Here is the highest-quality quantization of each model that fits common GPU memory budgets, so you can match Mistral Small 4 or Qwen 3 32B to the card you actually own:
- On a 24 GB GPU: Mistral Small 4 does not fit; Qwen 3 32B runs at Q5 (23 GB).
Benchmark scores
Reported benchmarks for Qwen 3 32B: MMLU-Pro 65.54, SuperGPQA 39.78.
Bottom line: which should you pick?
- Pick Mistral Small 4 for long-context work (up to 250k tokens).
- Pick Qwen 3 32B for lower VRAM and faster inference; pick Mistral Small 4 for maximum headline quality.
- Pick Mistral Small 4 if your workload is code, fr, moe, vision.
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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Frequently asked questions
What is the difference between Mistral Small 4 and Qwen 3 32B?
The headline differences: Mistral Small 4 is a 119B model and Qwen 3 32B is 32B; their context windows differ (250k vs 128k tokens). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.
Can Mistral Small 4 and Qwen 3 32B run on a 24 GB GPU?
At a Q4 quantization, Mistral Small 4 needs about 72 GB of VRAM and needs more than 24 GB (multi-GPU or heavier offload); Qwen 3 32B needs about 19 GB and fits comfortably on a 24 GB GPU. Qwen 3 32B is the lighter option for tight VRAM budgets.
Which is faster, Mistral Small 4 or Qwen 3 32B?
Qwen 3 32B is the smaller model (32B vs 119B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.
What licenses do Mistral Small 4 and Qwen 3 32B use?
Mistral Small 4 is licensed under Apache 2.0 and Qwen 3 32B under Apache 2.0.
Which has the longer context window, Mistral Small 4 or Qwen 3 32B?
Mistral Small 4 has the larger context window (250k vs 128k tokens), so it handles longer documents and codebases in a single prompt.