Overview
In the rapidly evolving landscape of Large Language Models, selecting the right architecture for software development tasks is critical. This comparative analysis evaluates OpenAI: GPT-5.4 Mini vs Qwen: Qwen3.5 397B A17B within the specific context of Coding Performance with 10 Evaluators. By utilizing PeerLM's rigorous evaluation framework, we identify which model better handles complex technical instructions and code generation tasks.
Benchmark Results
The evaluation reveals a clear distinction between the two models. OpenAI: GPT-5.4 Mini demonstrates superior proficiency in coding tasks, achieving an overall score of 7.95, significantly outperforming the Qwen: Qwen3.5 397B A17B model in this specific benchmark run.
| Model | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|
| OpenAI: GPT-5.4 Mini | 7.95 | 7.95 | 7.95 |
| Qwen: Qwen3.5 397B A17B | 2.05 | 2.05 | 2.05 |
Criteria Breakdown
The evaluation focused on two key pillars: Accuracy and Instruction Following. In the realm of coding, these metrics are vital for ensuring that generated snippets are not only syntactically correct but also align with the developer's specific intent.
- Accuracy: OpenAI: GPT-5.4 Mini provided highly reliable code segments, while Qwen: Qwen3.5 397B A17B struggled to maintain consistent logical correctness under the constraints of the 10-evaluator panel.
- Instruction Following: The ability to adhere to strict coding style guides and framework requirements was a key differentiator. GPT-5.4 Mini maintained high fidelity to the testing prompts throughout the cycle.
Cost & Latency
Efficiency is a major consideration for production-grade coding assistants. Below is the breakdown of the cost and token usage during our test cycle.
| Model | Total Cost (USD) | Avg Completion Tokens | Cost/Output Token |
|---|---|---|---|
| OpenAI: GPT-5.4 Mini | $0.003548 | 161 | $0.005501 |
| Qwen: Qwen3.5 397B A17B | $0.025549 | 2691 | $0.002374 |
While Qwen: Qwen3.5 397B A17B features a lower cost per individual output token, its significantly higher completion volume resulted in a higher total cost per request compared to the more concise GPT-5.4 Mini.
Use Cases
OpenAI: GPT-5.4 Mini is currently the recommended choice for tasks requiring high-precision code generation where accuracy is non-negotiable. Its performance in this benchmark suggests it is well-suited for code completion, unit test generation, and debugging assistance. Qwen: Qwen3.5 397B A17B may be better suited for exploratory tasks or scenarios where a larger context window and longer-form generation are prioritized over strict adherence to technical accuracy.
Verdict
When comparing OpenAI: GPT-5.4 Mini vs Qwen: Qwen3.5 397B A17B, the former emerges as the clear leader for technical coding tasks. With a score spread of 5.9, GPT-5.4 Mini provides a more stable and reliable output for developers, justifying its place at the top of our current leaderboard.