Overview
In this technical evaluation, we pit OpenAI: GPT-5.4 vs Qwen: Qwen3.5 397B A17B against each other to determine which model reigns supreme in real-world coding scenarios. Using PeerLM's specialized Coding Performance with 10 Evaluators suite, we assessed these models on their ability to handle complex programming logic, syntax accuracy, and instruction following. As the demand for AI-assisted development grows, choosing the right model based on objective, multi-evaluator benchmarks is essential for engineering productivity.
Benchmark Results
The evaluation results highlight a significant performance gap between the two models in our coding-specific test environment. The leaderboard reflects the consensus of 10 independent evaluators who assessed the output quality of each model.
| Model | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|
| OpenAI: GPT-5.4 | 7.37 | 7.37 | 7.37 |
| Qwen: Qwen3.5 397B A17B | 2.63 | 2.63 | 2.63 |
Criteria Breakdown
The evaluation focused on two primary pillars: Accuracy and Instruction Following. In the context of writing code, these criteria are non-negotiable. OpenAI: GPT-5.4 demonstrated a consistent ability to generate functional code that adhered strictly to the provided prompts, resulting in an overall score of 7.37. Conversely, Qwen: Qwen3.5 397B A17B struggled to maintain the same level of precision, scoring 2.63 across both categories. The 4.74 point score spread clearly indicates a divergence in how these models process complex algorithmic requirements.
Cost & Latency
Cost efficiency is a critical factor for teams integrating AI into CI/CD pipelines or IDE extensions. Below is the breakdown of the investment required for these models during our benchmark run:
- OpenAI: GPT-5.4: Total cost of $0.010055, with an average of 132 completion tokens per response.
- Qwen: Qwen3.5 397B A17B: Total cost of $0.025549, with a much higher output volume averaging 2,691 completion tokens per response and an average latency of 751ms.
While GPT-5.4 is more efficient in its output length, Qwen's tendency to generate significantly longer completions contributes to its higher total cost per request in this specific evaluation.
Use Cases
For developers looking for high-accuracy code generation, OpenAI: GPT-5.4 is currently the superior choice for critical tasks like boilerplate generation, bug fixing, and complex logic implementation. Its ability to follow instructions precisely ensures that the code produced is closer to production-ready status. Qwen: Qwen3.5 397B A17B, while showing a different operational profile, may be better suited for tasks requiring verbose documentation or exploratory code generation where brevity is less of a concern than absolute accuracy.
Verdict
The comparative evaluation of OpenAI: GPT-5.4 vs Qwen: Qwen3.5 397B A17B reveals a clear leader in coding performance. GPT-5.4 outperformed the Qwen model in both accuracy and adherence to specific coding instructions, making it the more reliable partner for software engineering workflows.