Qwen 2.5 Coder 32BvsCodestral 22B v0.1
Complete comparison between Qwen 2.5 Coder 32B (32B parameters, Alibaba) and Codestral 22B v0.1 (22B, Mistral AI). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data — no copy-pasting between pages.
In brief
| Feature | Qwen 2.5 Coder 32B | Codestral 22B v0.1 |
|---|---|---|
| Settings | 32B | 22B |
| Family | Qwen | Mistral |
| Author | Alibaba | Mistral AI |
| Origin | CN | FR |
| License | Apache 2.0 | Mistral Non-Production License |
| Context | 131,072 tokens | 32,000 tokens |
| Output | November 2024 | May 2024 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Qwen 2.5 Coder 32B | Codestral 22B v0.1 |
|---|---|---|
| Q4_K_M (lightweight) | 19 GB | 13 GB |
| Q5_K_M (balanced) | 23 GB | 16 GB |
| Q8 (near-lossless) | 35 GB | 24 GB |
| FP16 (max quality) | 64 GB | 44 GB |
| CPU-only RAM | 32 GB | 22 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Qwen 2.5 Coder 32B | Codestral 22B v0.1 |
|---|---|---|
| RTX 4090 (24 GB) | 30 tok/s · Q5_K_M | 42 tok/s · Q8 |
| RTX 4080 (16 GB) | ✗ too heavy | 16 tok/s · Q5_K_M |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q8 | 16 tok/s · Q8 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Qwen 2.5 Coder 32B
The open-weight code reference from late 2024, now surpassed by the Qwen3-Coder / Devstral generation.
- Best open-weight coding LLM
- Very strong on HumanEval (2024 benchmark)
- 128k context
- Requires 20+ GB of VRAM
- Worse at general chat
Installation
Codestral 22B v0.1
Code 22B Mistral, 80+ languages. ⚠ MNPL non-production license — personal/research use.
- Excellent at coding
- HumanEval 81.1, MBPP 78.2
- ⚠ MNPL license (non-production)
- Personal/research use only
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Qwen 2.5 Coder 32B or Codestral 22B v0.1?
On an RTX 4090, Qwen 2.5 Coder 32B runs at Q5_K_M (~30 tok/s), Codestral 22B v0.1 in Q8 (~42 tok/s). In pure throughput, Codestral 22B v0.1 wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Codestral 22B v0.1 fits in 13 GB versus 19 GB for its rival—a 6 GB gap, significant if you are targeting an RTX 3060 or an 8 GB 4060 Ti.
Qwen 2.5 Coder 32B or Codestral 22B v0.1 for coding?
Both are specialized code models — the choice depends on context (131 072 vs 32 000 tokens), license (Apache 2.0 vs Mistral Non-Production License), and available VRAM. Both integrate with Continue or Aider.
Can these models be used in commercial production?
Qwen 2.5 Coder 32B is under Apache 2.0 — free for commercial use. Codestral 22B v0.1 is under Mistral Non-Production License — also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Codestral 22B v0.1 (22B). More capable: Qwen 2.5 Coder 32B (32B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.