Devstral Small 2 24B vs Qwen 2.5 Coder 32B
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Devstral Small 2 24B | Qwen 2.5 Coder 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 | code, fr | code |
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 Devstral Small 2 24B
Mistral AI's 24B coding specialist co-developed with All Hands AI, scoring 72.2% on SWE-Bench under Apache 2.0. Fits on a single RTX 4090. Strengths: 72.2% SWE-Bench in a 24B dense model, Runs comfortably on a single RTX 4090, 256K context for whole-repo work, Apache 2.0 license.
About Qwen 2.5 Coder 32B
Alibaba's Qwen 2.5 Coder 32B — the strongest open-weight code model we've benchmarked, trading punches with Claude 3.5 Sonnet on HumanEval. Strengths: Best-in-class open-weight code generation, Claude 3.5 Sonnet-level HumanEval scores, 128k context for repo-wide tasks, Apache 2.0 license.
How they compare
Devstral Small 2 24B comes from Mistral AI and Qwen 2.5 Coder 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 Coder 32B is the larger of the two. At Q4, Devstral Small 2 24B fits in about 14 GB of VRAM versus 19 GB for the other — a 5 GB difference that matters on consumer GPUs.
The two models target different sweet spots: Devstral Small 2 24B is tuned for code, fr, while Qwen 2.5 Coder 32B leans toward code. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Devstral Small 2 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, Devstral Small 2 24B offers the bigger window (250k vs 128k tokens).
Memory, quantization & throughput
Across quantization levels, Devstral Small 2 24B requires Q4 ≈ 14 GB, Q5 ≈ 17 GB, Q8 ≈ 26 GB, FP16 ≈ 48 GB, while Qwen 2.5 Coder 32B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB. In practice Devstral Small 2 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, Devstral Small 2 24B needs roughly 24 GB of system RAM to run on CPU and Qwen 2.5 Coder 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 Devstral Small 2 24B and 12 from Qwen 2.5 Coder 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 Devstral Small 2 24B or Qwen 2.5 Coder 32B to the card you actually own:
- On a 16 GB GPU: Devstral Small 2 24B runs at Q4 (14 GB); Qwen 2.5 Coder 32B does not fit.
- On a 24 GB GPU: Devstral Small 2 24B runs at Q5 (17 GB); Qwen 2.5 Coder 32B runs at Q5 (23 GB).
Benchmark scores
Reported benchmarks for Devstral Small 2 24B: SWE-Bench 72.2.
Reported benchmarks for Qwen 2.5 Coder 32B: HumanEval 92.7, MBPP 86, LiveCodeBench 31.4.
Bottom line: which should you pick?
- Pick Devstral Small 2 24B for long-context work (up to 250k tokens).
- Pick Devstral Small 2 24B for lower VRAM and faster inference; pick Qwen 2.5 Coder 32B for maximum headline quality.
- Pick Devstral Small 2 24B if your workload is fr.
Which GPU should you buy to run Qwen 2.5 Coder 32B?
To run Qwen 2.5 Coder 32B locally at Q4, you need ~19 GB of VRAM. The best value for this is a RTX 4090 (24 GB VRAM).
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Frequently asked questions
What is the difference between Devstral Small 2 24B and Qwen 2.5 Coder 32B?
The headline differences: Devstral Small 2 24B is a 24B model and Qwen 2.5 Coder 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 Devstral Small 2 24B and Qwen 2.5 Coder 32B run on a 24 GB GPU?
At a Q4 quantization, Devstral Small 2 24B needs about 14 GB of VRAM and fits comfortably on a 24 GB GPU; Qwen 2.5 Coder 32B needs about 19 GB and fits comfortably on a 24 GB GPU. Devstral Small 2 24B is the lighter option for tight VRAM budgets.
Which is faster, Devstral Small 2 24B or Qwen 2.5 Coder 32B?
Devstral Small 2 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 Devstral Small 2 24B and Qwen 2.5 Coder 32B use?
Devstral Small 2 24B is licensed under Apache 2.0 and Qwen 2.5 Coder 32B under Apache 2.0.
Which has the longer context window, Devstral Small 2 24B or Qwen 2.5 Coder 32B?
Devstral Small 2 24B has the larger context window (250k vs 128k tokens), so it handles longer documents and codebases in a single prompt.