Devstral Small 2 24B vs Qwen 2.5 Coder 14B Instruct
Side-by-side specs, benchmarks, and a verdict by use case.
Updated 2026-07-13
| Spec | Devstral Small 2 24B | Qwen 2.5 Coder 14B Instruct |
|---|---|---|
| Parameters | 24B | 14B |
| Author | Mistral AI | Alibaba |
| License | Apache 2.0 | Apache 2.0 |
| Context window | 0k | 0k |
| VRAM at Q4 | 14 GB | 9 GB |
| VRAM at Q5 | 17 GB | 11 GB |
| VRAM at Q8 | 26 GB | 16 GB |
| VRAM at FP16 | 48 GB | 28 GB |
| Use cases | code, fr | code |
Verdict
Devstral Small 2 24B is significantly larger (24B vs 14B), so expect higher quality but heavier VRAM and slower throughput.
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 14B Instruct
Alibaba's Qwen 2.5 Coder 14B under Apache 2.0 with HumanEval 89.6 and LiveCodeBench 37.1. The VRAM sweet spot for serious self-hosted code generation. Strengths: HumanEval 89.6 — competitive with much larger coders, LiveCodeBench 37.1, Apache 2.0 license, 131k context for long-file work.
How they compare
Devstral Small 2 24B comes from Mistral AI and Qwen 2.5 Coder 14B Instruct 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 14B parameters, Devstral Small 2 24B is the larger of the two. At Q4, Qwen 2.5 Coder 14B Instruct fits in about 9 GB of VRAM versus 14 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 14B Instruct leans toward code. Match the model to your dominant workload rather than to raw size.
On a typical mid-range GPU, Qwen 2.5 Coder 14B Instruct pushes roughly 20 tokens/sec versus 15, 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 14B Instruct requires Q4 ≈ 9 GB, Q5 ≈ 11 GB, Q8 ≈ 16 GB, FP16 ≈ 28 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 14B Instruct about 16 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 20 from Qwen 2.5 Coder 14B Instruct, scaling up to 40 and 55 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 14B Instruct to the card you actually own:
- On a 12 GB GPU: Devstral Small 2 24B does not fit; Qwen 2.5 Coder 14B Instruct runs at Q5 (11 GB).
- On a 16 GB GPU: Devstral Small 2 24B runs at Q4 (14 GB); Qwen 2.5 Coder 14B Instruct runs at Q8 (16 GB).
- On a 24 GB GPU: Devstral Small 2 24B runs at Q5 (17 GB); Qwen 2.5 Coder 14B Instruct runs at Q8 (16 GB).
Benchmark scores
Reported benchmarks for Devstral Small 2 24B: SWE-Bench 72.2.
Reported benchmarks for Qwen 2.5 Coder 14B Instruct: HumanEval 89.6, MBPP 86.2, LiveCodeBench 37.1.
Bottom line: which should you pick?
- Pick Devstral Small 2 24B for long-context work (up to 250k tokens).
- Pick Qwen 2.5 Coder 14B Instruct for lower VRAM and faster inference; pick Devstral Small 2 24B for maximum headline quality.
- Pick Devstral Small 2 24B if your workload is fr.
Which GPU should you buy to run Devstral Small 2 24B?
To run Devstral Small 2 24B locally at Q4, you need ~14 GB of VRAM. The best value for this is a RTX 5070 Ti (16 GB VRAM).
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Frequently asked questions
What is the difference between Devstral Small 2 24B and Qwen 2.5 Coder 14B Instruct?
The headline differences: Devstral Small 2 24B is a 24B model and Qwen 2.5 Coder 14B Instruct is 14B; 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 14B Instruct 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 14B Instruct needs about 9 GB and fits comfortably on a 24 GB GPU. Qwen 2.5 Coder 14B Instruct is the lighter option for tight VRAM budgets.
Which is faster, Devstral Small 2 24B or Qwen 2.5 Coder 14B Instruct?
Qwen 2.5 Coder 14B Instruct is the smaller model (14B vs 24B), 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 14B Instruct use?
Devstral Small 2 24B is licensed under Apache 2.0 and Qwen 2.5 Coder 14B Instruct under Apache 2.0.
Which has the longer context window, Devstral Small 2 24B or Qwen 2.5 Coder 14B Instruct?
Devstral Small 2 24B has the larger context window (250k vs 128k tokens), so it handles longer documents and codebases in a single prompt.