Mistral Small 3.1 24B vs Qwen 2.5 32B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Mistral Small 3.1 24B | Qwen 2.5 32B |
|---|---|---|
| Parameters | 24B | 32B |
| Author | Mistral AI | Alibaba |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 14 GB | 19 GB |
| VRAM at Q5 | 17 GB | 23 GB |
| VRAM at Q8 | 26 GB | 35 GB |
| VRAM at FP16 | 48 GB | 64 GB |
| Use cases | chat, general, vision, multilingual, fr | chat, general |
Verdict
Both models sit in a similar size class. The pick depends on tags, license, and benchmarks rather than raw parameter count.
The two models at a glance
About Mistral Small 3.1 24B
Mistral AI's Small 3.1 — Small 3 plus a vision encoder, a 128k context, and ~150 tok/s inference under Apache 2.0. Small 3.2 (June 2025) is a drop-in upgrade. Strengths: Vision and text combined in one 24B model, 128k context window, Apache 2.0 license, Around 150 tokens/sec inference.
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 3.1 24B 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 3.1 24B 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 MMLU with 83.3 against 80.6 — a narrow 2.7-point margin. For workloads weighted toward that benchmark, Qwen 2.5 32B is the stronger default.
On a typical mid-range GPU, Mistral Small 3.1 24B 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 125k tokens).
Memory, quantization & throughput
Across quantization levels, Mistral Small 3.1 24B 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 3.1 24B 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 3.1 24B 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 3.1 24B 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 3.1 24B or Qwen 2.5 32B to the card you actually own:
- On a 16 GB GPU: Mistral Small 3.1 24B runs at Q4 (14 GB); Qwen 2.5 32B does not fit.
- On a 24 GB GPU: Mistral Small 3.1 24B runs at Q5 (17 GB); Qwen 2.5 32B runs at Q5 (23 GB).
Benchmark scores
Reported benchmarks for Mistral Small 3.1 24B: MMLU 80.6, MMMU 64.
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 3.1 24B for lower VRAM and faster inference; pick Qwen 2.5 32B for maximum headline quality.
- Pick Qwen 2.5 32B if MMLU performance is your priority (83.3 vs 80.6).
- Pick Mistral Small 3.1 24B if your workload is fr, multilingual, vision.
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).
As an Amazon Associate, BestLLMfor earns from qualifying purchases, at no extra cost to you. It does not influence our independent rankings.
Frequently asked questions
What is the difference between Mistral Small 3.1 24B and Qwen 2.5 32B?
The headline differences: Mistral Small 3.1 24B is a 24B model and Qwen 2.5 32B is 32B; their context windows differ (125k 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 3.1 24B and Qwen 2.5 32B run on a 24 GB GPU?
At a Q4 quantization, Mistral Small 3.1 24B 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 3.1 24B is the lighter option for tight VRAM budgets.
Is Mistral Small 3.1 24B or Qwen 2.5 32B more capable?
On MMLU, Qwen 2.5 32B scores higher (83.3 vs 80.6), a 2.7-point advantage on this benchmark.
Which is faster, Mistral Small 3.1 24B or Qwen 2.5 32B?
Mistral Small 3.1 24B 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 3.1 24B and Qwen 2.5 32B use?
Mistral Small 3.1 24B is licensed under Apache 2.0 and Qwen 2.5 32B under Apache 2.0.
Which has the longer context window, Mistral Small 3.1 24B or Qwen 2.5 32B?
Qwen 2.5 32B has the larger context window (128k vs 125k tokens), so it handles longer documents and codebases in a single prompt.