Overview
In the rapidly evolving landscape of Large Language Models, choosing the right architecture for programming tasks is critical. This evaluation focuses on Qwen: Qwen3 32B vs Mistral: Mistral Small 3.2 24B, specifically measuring their Coding Performance with 10 Evaluators. By utilizing PeerLM’s comparative ranking methodology, we provide a clear picture of how these models perform when tasked with writing, debugging, and refining code snippets.
Benchmark Results
The evaluation was conducted using a comparative ranking system, where 10 independent evaluators assessed the outputs of both models. The results highlight a distinct leader in terms of consistency and quality in coding tasks.
| Model | Overall Score | Accuracy | Instruction Following | Avg Completion (Tokens) |
|---|---|---|---|---|
| Mistral: Mistral Small 3.2 24B | 6.58 | 6.58 | 6.58 | 152 |
| Qwen: Qwen3 32B | 3.42 | 3.42 | 3.42 | 85 |
Criteria Breakdown
The evaluation centered on two primary pillars: Accuracy and Instruction Following. In coding, these metrics are vital; a model that follows instructions perfectly but produces inaccurate code is as useless as one that writes correct logic while ignoring the user's constraints. Mistral: Mistral Small 3.2 24B demonstrated a higher capacity to adhere to complex coding prompts, resulting in a significantly higher overall score compared to Qwen: Qwen3 32B.
Cost & Latency
Understanding the economic and performance trade-offs is essential for production-level implementation. While Qwen: Qwen3 32B has a lower total cost per request, the output token cost reveals a different story, making Mistral: Mistral Small 3.2 24B more efficient for generating longer, more descriptive code blocks.
- Qwen: Qwen3 32B: Total cost of $0.000152 per response, with a cost per output token of $0.000447.
- Mistral: Mistral Small 3.2 24B: Total cost of $0.000191 per response, with a cost per output token of $0.000315.
The average completion length for Mistral (152 tokens) is nearly double that of Qwen (85 tokens), suggesting that Mistral is more verbose and potentially more helpful in providing full context in coding solutions.
Use Cases
Mistral: Mistral Small 3.2 24B is best suited for complex development environments where instruction adherence and comprehensive code generation are non-negotiable. Its higher ranking in the Coding Performance with 10 Evaluators suite makes it a reliable choice for IDE integrations and automated code review tools.
Qwen: Qwen3 32B remains a viable candidate for lighter programming tasks or scenarios where budget constraints are the primary driver, provided the complexity of the requested logic is relatively low.
Verdict
Based on the current evaluation metrics, Mistral: Mistral Small 3.2 24B is the clear winner for coding-specific applications. With a score of 6.58 compared to 3.42, it demonstrates superior logic and instruction following, making it the more dependable model for professional development workflows.