Mistral Small 3 vs Qwen 2.5 32B

The two models at a glance

About Mistral Small 3

Mistral AI's 24B dense model that closes most of the gap with 70B-class models. Best quality-per-parameter we've measured at this size in 2025. Strengths: Quality approaching Llama 3 70B at a third the size, Low latency relative to peers, 128k context window, Strong tool use and agent behavior.

About Qwen 2.5 32B

Alibaba's Qwen 2.5 32B, the open-weight 32B reference of late 2024 — matching 70B-class quality on most benchmarks at half the VRAM. Strengths: Quality on par with many 70B models, 128k context, Apache 2.0 license, Strong math, code, and reasoning.

How they compare

Mistral Small 3 comes from Mistral AI and Qwen 2.5 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 24B vs 32B parameters, Qwen 2.5 32B is the larger of the two. At Q4, Mistral Small 3 fits in about 14 GB of VRAM versus 19 GB for the other — a 5 GB difference that matters on consumer GPUs.

Where they overlap on benchmarks, Qwen 2.5 32B takes HumanEval with 90.2 against 84.8 — a clear 5.4-point margin. On MMLU the edge goes to Qwen 2.5 32B (83.3 vs 81). For workloads weighted toward that benchmark, Qwen 2.5 32B is the stronger default.

On a typical mid-range GPU, Mistral Small 3 pushes roughly 15 tokens/sec versus 12, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 2.5 32B offers the bigger window (128k vs 32k tokens).

Memory, quantization & throughput

Across quantization levels, Mistral Small 3 requires Q4 ≈ 14 GB, Q5 ≈ 17 GB, Q8 ≈ 26 GB, FP16 ≈ 48 GB, while Qwen 2.5 32B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB. In practice Mistral Small 3 needs a 16 GB card 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 3 needs roughly 24 GB of system RAM to run on CPU and Qwen 2.5 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 15 tokens/sec from Mistral Small 3 and 12 from Qwen 2.5 32B, scaling up to 40 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 3 or Qwen 2.5 32B to the card you actually own:

• On a 16 GB GPU: Mistral Small 3 runs at Q4 (14 GB); Qwen 2.5 32B does not fit.
• On a 24 GB GPU: Mistral Small 3 runs at Q5 (17 GB); Qwen 2.5 32B runs at Q5 (23 GB).

Benchmark scores

Reported benchmarks for Mistral Small 3: MMLU 81, GPQA 42.2, HumanEval 84.8.

Reported benchmarks for Qwen 2.5 32B: MMLU 83.3, HumanEval 90.2, MATH 83.1.

Bottom line: which should you pick?

• Pick Qwen 2.5 32B for long-context work (up to 128k tokens).
• Pick Mistral Small 3 for lower VRAM and faster inference; pick Qwen 2.5 32B for maximum headline quality.
• Pick Qwen 2.5 32B if HumanEval performance is your priority (90.2 vs 84.8).
• Pick Mistral Small 3 if your workload is code.

Which GPU should you buy to run Qwen 2.5 32B?

To run Qwen 2.5 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).

Frequently asked questions

What is the difference between Mistral Small 3 and Qwen 2.5 32B?

The headline differences: Mistral Small 3 is a 24B model and Qwen 2.5 32B is 32B; their context windows differ (32k 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 3 and Qwen 2.5 32B run on a 24 GB GPU?

At a Q4 quantization, Mistral Small 3 needs about 14 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 2.5 32B needs about 19 GB and fits comfortably on a 24 GB GPU. Mistral Small 3 is the lighter option for tight VRAM budgets.

Mistral Small 3 vs Qwen 2.5 32B for coding — which is better?

On HumanEval, Qwen 2.5 32B leads with 90.2 vs 84.8 (a 5.4-point gap), making it the stronger pick for code generation.

Which is faster, Mistral Small 3 or Qwen 2.5 32B?

Mistral Small 3 is the smaller model (24B vs 32B), 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 3 and Qwen 2.5 32B use?

Mistral Small 3 is licensed under Apache 2.0 and Qwen 2.5 32B under Apache 2.0.

Which has the longer context window, Mistral Small 3 or Qwen 2.5 32B?

Qwen 2.5 32B has the larger context window (128k vs 32k tokens), so it handles longer documents and codebases in a single prompt.