Overview
In the rapidly evolving landscape of large language models, selecting the right architecture for software development tasks is critical. This comparative analysis focuses on Qwen: Qwen3.5 397B A17B vs Z.ai: GLM 5, specifically evaluating their mastery of code generation, debugging, and instruction adherence through the PeerLM Coding Performance with 10 Evaluators suite. By leveraging a panel of 10 independent evaluators, we provide a neutral, ranking-based assessment of how these models perform in real-world coding scenarios.
Benchmark Results
The evaluation results indicate a clear hierarchy in performance for this specific coding suite. Z.ai: GLM 5 has emerged as the top performer, demonstrating superior alignment with evaluator expectations compared to the Qwen: Qwen3.5 397B A17B model.
| Model | Rank | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|---|
| Z.ai: GLM 5 | 1 | 6.67 | 6.67 | 6.67 |
| Qwen: Qwen3.5 397B A17B | 2 | 3.33 | 3.33 | 3.33 |
Criteria Breakdown
The evaluation utilized two primary pillars: Accuracy and Instruction Following. The comparative nature of this study highlights how well each model translates complex coding prompts into functional, clean, and compliant code.
- Accuracy: Z.ai: GLM 5 achieved a score of 6.67, effectively outperforming Qwen: Qwen3.5 397B A17B, which recorded a 3.33. This suggests that the GLM 5 architecture is significantly more reliable when handling nuanced programming logic and syntax requirements.
- Instruction Following: In software development, the ability to follow specific architectural constraints or framework requirements is paramount. Z.ai: GLM 5 maintained consistency across all 10 evaluators, securing a 6.67 score, doubling the performance metric of its counterpart.
Cost & Latency
Efficiency is a major consideration for enterprise-scale deployments. Understanding the cost-to-performance ratio is essential when integrating these models into CI/CD pipelines or IDE extensions.
| Model | Total Cost (USD) | Avg Completion Tokens | Cost per Output Token |
|---|---|---|---|
| Z.ai: GLM 5 | $0.009623 | 976 | $0.002465 |
| Qwen: Qwen3.5 397B A17B | $0.025549 | 2691 | $0.002374 |
While Qwen: Qwen3.5 397B A17B generates a higher volume of completion tokens, Z.ai: GLM 5 provides a more cost-effective solution, with a total experiment cost of $0.009623 compared to $0.025549.
Use Cases
Z.ai: GLM 5 is recommended for high-stakes development environments where precision and strict adherence to coding standards are non-negotiable. Its performance in this benchmark suggests it is well-suited for automated code review, boilerplate generation, and complex refactoring tasks.
Qwen: Qwen3.5 397B A17B, while ranking second in this specific comparative suite, remains a powerful candidate for tasks requiring verbose documentation or extensive code expansion, given its higher average completion token count per response.
Verdict
In this iteration of Coding Performance with 10 Evaluators, Z.ai: GLM 5 establishes itself as the more capable and cost-efficient option for developers. Its higher consistency in both accuracy and instruction-following makes it the preferred model for technical workflows.