Qwen 2.5 Coder 32B vs Codestral 22B v0.1

The two models at a glance

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.

About Codestral 22B v0.1

Mistral AI's 22B code specialist covering 80+ programming languages, with strong HumanEval and MBPP scores. Locked behind the restrictive MNPL license — personal and research use only. Strengths: HumanEval 81.1 and MBPP 78.2 — competitive with much larger models at release, Broad language coverage including niche languages, 32k context handles most repo files comfortably, Strong fill-in-the-middle completion.

How they compare

Qwen 2.5 Coder 32B comes from Alibaba and Codestral 22B v0.1 from Mistral AI, they belong to the Qwen and Mistral 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 32B vs 22B parameters, Qwen 2.5 Coder 32B is the larger of the two. At Q4, Codestral 22B v0.1 fits in about 13 GB of VRAM versus 19 GB for the other — a 6 GB difference that matters on consumer GPUs.

Where they overlap on benchmarks, Qwen 2.5 Coder 32B takes HumanEval with 92.7 against 81.1 — a decisive 11.6-point margin. On MBPP the edge goes to Qwen 2.5 Coder 32B (86 vs 78.2). For workloads weighted toward that benchmark, Qwen 2.5 Coder 32B is the stronger default.

On a typical mid-range GPU, Codestral 22B v0.1 pushes roughly 16 tokens/sec versus 12, so it is the more responsive choice for interactive or high-volume use. For long-context work, Qwen 2.5 Coder 32B offers the bigger window (128k vs 31k tokens).

Memory, quantization & throughput

Across quantization levels, Qwen 2.5 Coder 32B requires Q4 ≈ 19 GB, Q5 ≈ 23 GB, Q8 ≈ 35 GB, FP16 ≈ 64 GB, while Codestral 22B v0.1 requires Q4 ≈ 13 GB, Q5 ≈ 16 GB, Q8 ≈ 24 GB, FP16 ≈ 44 GB. In practice Qwen 2.5 Coder 32B wants a 24 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, Qwen 2.5 Coder 32B needs roughly 32 GB of system RAM to run on CPU and Codestral 22B v0.1 about 22 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 Qwen 2.5 Coder 32B and 16 from Codestral 22B v0.1, scaling up to 30 and 42 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 Qwen 2.5 Coder 32B or Codestral 22B v0.1 to the card you actually own:

• On a 16 GB GPU: Qwen 2.5 Coder 32B does not fit; Codestral 22B v0.1 runs at Q5 (16 GB).
• On a 24 GB GPU: Qwen 2.5 Coder 32B runs at Q5 (23 GB); Codestral 22B v0.1 runs at Q8 (24 GB).

Benchmark scores

Reported benchmarks for Qwen 2.5 Coder 32B: HumanEval 92.7, MBPP 86, LiveCodeBench 31.4.

Reported benchmarks for Codestral 22B v0.1: HumanEval 81.1, MBPP 78.2, Spider 63.5.

Bottom line: which should you pick?

• Pick Qwen 2.5 Coder 32B if you need a permissive (Apache 2.0) license for commercial deployment.
• Pick Qwen 2.5 Coder 32B for long-context work (up to 128k tokens).
• Pick Codestral 22B v0.1 for lower VRAM and faster inference; pick Qwen 2.5 Coder 32B for maximum headline quality.
• Pick Qwen 2.5 Coder 32B if HumanEval performance is your priority (92.7 vs 81.1).
• Pick Codestral 22B v0.1 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).

Frequently asked questions

What is the difference between Qwen 2.5 Coder 32B and Codestral 22B v0.1?

The headline differences: Qwen 2.5 Coder 32B is a 32B model and Codestral 22B v0.1 is 22B; their context windows differ (128k vs 31k tokens); they ship under different licenses (Apache 2.0 vs Mistral Non-Production License). Below we break down VRAM by quantization, benchmark scores, and a use-case verdict so you can pick the right one.

Can Qwen 2.5 Coder 32B and Codestral 22B v0.1 run on a 24 GB GPU?

At a Q4 quantization, Qwen 2.5 Coder 32B needs about 19 GB of VRAM and fits comfortably on a 24 GB GPU; Codestral 22B v0.1 needs about 13 GB and fits comfortably on a 24 GB GPU. Codestral 22B v0.1 is the lighter option for tight VRAM budgets.

Qwen 2.5 Coder 32B vs Codestral 22B v0.1 for coding — which is better?

On HumanEval, Qwen 2.5 Coder 32B leads with 92.7 vs 81.1 (a 11.6-point gap), making it the stronger pick for code generation.

Which is faster, Qwen 2.5 Coder 32B or Codestral 22B v0.1?

Codestral 22B v0.1 is the smaller model (22B vs 32B), so on the same hardware it runs faster and uses less memory. The larger model trades speed for headline quality.

Which license is safer for commercial use, Qwen 2.5 Coder 32B or Codestral 22B v0.1?

Qwen 2.5 Coder 32B ships under Apache 2.0, a permissive license with no usage restrictions, whereas the other is under Mistral Non-Production License — check its terms before commercial deployment.

Which has the longer context window, Qwen 2.5 Coder 32B or Codestral 22B v0.1?

Qwen 2.5 Coder 32B has the larger context window (128k vs 31k tokens), so it handles longer documents and codebases in a single prompt.