Codestral 22B v0.1vsQwen 2.5 Coder 7B
Complete comparison between Codestral 22B v0.1 (22B parameters, Mistral AI) and Qwen 2.5 Coder 7B (7B, Alibaba). 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 | Codestral 22B v0.1 | Qwen 2.5 Coder 7B |
|---|---|---|
| Settings | 22B | 7B |
| Family | Mistral | Qwen |
| Author | Mistral AI | Alibaba |
| Origin | FR | CN |
| License | Mistral Non-Production License | Apache 2.0 |
| Context | 32,000 tokens | 131,072 tokens |
| Output | May 2024 | November 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 | Codestral 22B v0.1 | Qwen 2.5 Coder 7B |
|---|---|---|
| Q4_K_M (lightweight) | 13 GB | 5 GB |
| Q5_K_M (balanced) | 16 GB | 6 GB |
| Q8 (near-lossless) | 24 GB | 9 GB |
| FP16 (max quality) | 44 GB | 16 GB |
| CPU-only RAM | 22 GB | 8 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 | Codestral 22B v0.1 | Qwen 2.5 Coder 7B |
|---|---|---|
| RTX 4090 (24 GB) | 42 tok/s · Q8 | 90 tok/s · FP16 |
| RTX 4080 (16 GB) | 16 tok/s · Q5_K_M | 35 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | ✗ too heavy | 12 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 16 tok/s · Q8 | 35 tok/s · FP16 |
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
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
Qwen 2.5 Coder 7B
Specialized in code. Competes with proprietary models on HumanEval.
- 128k context
- Apache 2.0
- Excellent code completion
- Long instructions supported
- Worse than the 32B on complex code
- Less versatile for general chat
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Codestral 22B v0.1 or Qwen 2.5 Coder 7B?
On an RTX 4090, Codestral 22B v0.1 runs in Q8 (~42 tok/s), Qwen 2.5 Coder 7B in FP16 (~90 tok/s). In pure throughput, Qwen 2.5 Coder 7B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 2.5 Coder 7B fits in 5 GB versus 13 GB for its rival — an 8 GB gap, significant if you’re targeting an RTX 3060 or an 8 GB 4060 Ti.
Codestral 22B v0.1 or Qwen 2.5 Coder 7B for coding?
Codestral 22B v0.1 is the best fit (code-specialized, top HumanEval scores). For an IDE copilot, pair it with Continue or Aider.
Can these models be used in commercial production?
Codestral 22B v0.1 is under Mistral Non-Production License — check the restrictions (especially the monthly user thresholds). Qwen 2.5 Coder 7B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 2.5 Coder 7B (7B). More capable: Codestral 22B v0.1 (22B). If you're unsure, run the 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.