Qwen 2.5 Coder 32B and Codestral 25.01 are the two strongest coding models you can run without paying for Claude or GPT. Qwen scores 88.4% on HumanEval — beating GPT-4. Codestral scores 95.3% on FIM pass@1 — the best autocomplete model available. They’re built for different things.
Quick comparison
| Qwen 2.5 Coder 32B | Codestral 25.01 | |
|---|---|---|
| Parameters | 32B | 22B |
| HumanEval (Python) | 88.4% | 86.6% |
| FIM pass@1 average | Not primary focus | 95.3% (SOTA) |
| Context window | 128K | 256K |
| Languages | 92 | 80+ |
| Training data | 5.5T code tokens | Undisclosed |
| License | Apache 2.0 | Mistral Non-Production |
| Self-host (free) | Yes | Restricted |
| API price (input) | Free (self-host) | $0.20/M |
Code generation: Qwen leads
On HumanEval, Qwen 2.5 Coder 32B scores 88.4% vs Codestral’s 86.6%. That 1.8% gap might seem small, but HumanEval at this level is competitive — Qwen is matching GPT-4o performance from a model you can run on a single 24GB GPU.
Qwen was trained on 5.5 trillion code-related tokens with supervised fine-tuning and reinforcement learning. It’s SOTA among open-source code models across more than 10 benchmarks including generation, completion, reasoning, and repair.
HuggingFace describes it as having “coding abilities matching those of GPT-4o” and being “a more comprehensive foundation for real-world applications such as Code Agents.”
Autocomplete (FIM): Codestral leads
For fill-in-the-middle — the task that powers IDE autocomplete — Codestral is clearly better. Its 95.3% FIM pass@1 average is the highest of any model, including closed ones. Codestral was specifically designed for this use case: low-latency, high-frequency code completion.
Qwen 2.5 Coder can do FIM, but it wasn’t optimized for it the way Codestral was. If your primary use case is IDE autocomplete suggestions, Codestral gives you better results.
Licensing: the real differentiator
This is where the decision gets easy for many developers:
- Qwen 2.5 Coder: Apache 2.0. Use it for anything. Commercial, personal, fine-tune it, embed it in your product. No restrictions.
- Codestral: Mistral Non-Production License. Free for research and non-commercial use. Commercial use requires a separate license from Mistral.
If you’re building a product that includes AI coding features, Qwen is the safe choice. If you’re just using it through an API for your own development, the license doesn’t matter.
Hardware requirements
- Qwen 2.5 Coder 32B: Needs ~20-24GB VRAM for Q4 quantization. Runs on an RTX 4090, A6000, or M-series Mac with 32GB+.
- Codestral 22B: Needs ~14-16GB VRAM for Q4. Runs on an RTX 4080 or equivalent. Lighter and faster.
Codestral’s smaller size means faster inference and lower hardware requirements. If you’re running on consumer hardware, Codestral is easier to deploy.
When to use each
Choose Qwen 2.5 Coder 32B if:
- You need the best overall code generation quality
- You’re building a commercial product with embedded AI
- You want Apache 2.0 licensing freedom
- You need to fine-tune the model on your codebase
- Code reasoning and repair matter as much as generation
Choose Codestral 25.01 if:
- IDE autocomplete is your primary use case
- You need the fastest possible FIM suggestions
- You’re using it through an API (licensing doesn’t matter)
- You want a lighter model that runs on less hardware
- You need 256K context for large repository understanding
Use both if:
- Codestral for real-time autocomplete in your IDE
- Qwen for code review, generation, and agent tasks
The bottom line
There’s no single “best” open-source coding model. Qwen 2.5 Coder 32B is the better code generator. Codestral 25.01 is the better autocomplete engine. The licensing difference makes Qwen the default for commercial use. The size difference makes Codestral the default for consumer hardware.
Most developers will get the best results using Codestral for autocomplete and Qwen for everything else.