Codestral 22B vs Qwen 2.5 Coder 32B Instruct

Qwen 2.5 Coder 32B outweighs Codestral 22B by 10B parameters and shows it in code benchmarks: on HumanEval it scores around 92%, compared to Codestral's strong but lower ~81%. The parameter gap translates to better multi-file reasoning and more reliable function signature generation — but you're also paying for the extra compute. At the time of writing, Qwen 2.5 Coder 32B runs roughly 1.5–2× more expensive per token than Codestral 22B on shared-inference providers.

Codestral 22B's context window sits at 32K tokens; Qwen 2.5 Coder 32B supports up to 128K. That 4× context advantage is decisive for certain workloads. Check live pricing on [Codestral 22B's model page](/models/mistralai--codestral-22b) to see how current rates compare.

For agentic coding workflows — where an AI coding assistant iterates over a large repo, ingests multiple files, and generates cross-file diffs — Qwen 2.5 Coder 32B's extended context and higher benchmark accuracy make it the clearer choice. The extra cost is likely absorbed by fewer retries and hallucinated imports.

Codestral 22B is the better call for IDE inline completion and single-function generation where latency matters more than deep-context reasoning. Its smaller footprint means providers can run it at lower cost with faster TTFT, and Mistral's fill-in-the-middle training data makes it particularly good at cursor-position completions. See the full provider list on [Qwen 2.5 Coder 32B's model page](/models/alibaba--qwen-2.5-coder-32b-instruct).

**Pick Codestral 22B** for low-latency completions and cost-sensitive inline suggestions. **Pick Qwen 2.5 Coder 32B** for agentic tasks, long-context repo analysis, and higher accuracy on complex generation.