Overview
In the rapidly evolving landscape of large language models, choosing the right architecture for software engineering tasks is critical. This report details the results of a comprehensive benchmark titled Coding Performance with 10 Evaluators. By leveraging human-expert comparative ranking, we provide a definitive look at how Meta: Llama 4 Maverick vs Qwen: Qwen3.5 397B A17B vs Z.ai: GLM 5 perform when tasked with complex programming challenges.
Benchmark Results
Our evaluation utilized a comparative ranking methodology, where 10 specialized evaluators assessed the models based on code accuracy and adherence to specific coding instructions. The following table summarizes the performance metrics and cost efficiency for each model.
| Model | Rank | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|---|
| Z.ai: GLM 5 | 1 | 8.21 | 8.21 | 8.21 |
| Qwen: Qwen3.5 397B A17B | 2 | 5.90 | 5.90 | 5.90 |
| Meta: Llama 4 Maverick | 3 | 0.90 | 0.90 | 0.90 |
Side-by-Side Performance Analysis
The comparative evaluation reveals a clear hierarchy in coding logic and syntax generation. Z.ai: GLM 5 emerged as the clear leader, consistently delivering code that met the rigorous standards of our 10 evaluators. Its ability to handle edge cases and maintain logical flow in complex functions set it apart from the competition.
Qwen: Qwen3.5 397B A17B provided solid performance, ranking second. It demonstrated a strong grasp of syntax but occasionally struggled with the nuanced instruction following required for highly specialized coding tasks. Meanwhile, Meta: Llama 4 Maverick, while highly efficient, lagged behind in this specific coding benchmark, suggesting it may be better suited for lighter-weight tasks rather than deep architectural coding.
Cost & Latency
For developers balancing quality with operational budgets, the cost-per-inference is a vital consideration. Below is the total cost breakdown for the evaluation run:
- Z.ai: GLM 5: $0.009623 per session
- Qwen: Qwen3.5 397B A17B: $0.025549 per session
- Meta: Llama 4 Maverick: $0.000358 per session
Use Cases
Based on our findings, Z.ai: GLM 5 is the recommended choice for production-grade coding assistants and complex debugging tasks. Qwen: Qwen3.5 397B A17B serves as a robust alternative for general-purpose development where high throughput is required. Meta: Llama 4 Maverick is best utilized in edge-computing scenarios or simple code-completion tasks where cost-efficiency is the primary driver.
Verdict
When comparing Meta: Llama 4 Maverick vs Qwen: Qwen3.5 397B A17B vs Z.ai: GLM 5, the data clearly favors Z.ai: GLM 5 for pure coding performance. While the other models offer different cost profiles, GLM 5 provides the most reliable and accurate coding assistance, making it the top choice for developers.