Overview
As the demand for high-quality code generation continues to surge, choosing the right Large Language Model (LLM) for software development workflows has become critical. In this evaluation, we compare Z.ai: GLM 5 vs xAI: Grok 4, focusing specifically on their Coding Performance with 10 Evaluators. By utilizing PeerLM’s comparative ranking methodology, we shed light on how these models handle complex programming tasks and instruction adherence.
Benchmark Results
Our evaluation utilized 10 expert human evaluators to rank output quality across two primary dimensions: Accuracy and Instruction Following. The results reveal a clear leader in this specific coding suite.
| Model | Overall Score | Accuracy | Instruction Following |
|---|---|---|---|
| Z.ai: GLM 5 | 6.32 | 6.32 | 6.32 |
| xAI: Grok 4 | 3.68 | 3.68 | 3.68 |
Criteria Breakdown
The evaluation focused on two key pillars of coding excellence:
- Accuracy: The ability of the model to generate syntactically correct and logically sound code snippets that solve the provided programming challenge.
- Instruction Following: The model's adherence to specific project constraints, such as using particular libraries, following existing code style guides, or implementing requested design patterns.
Z.ai: GLM 5 demonstrated superior consistency in these areas, achieving a score of 6.32 compared to the 3.68 achieved by xAI: Grok 4, indicating a significant lead in developer-centric reliability.
Cost & Latency
Beyond performance, operational costs are a primary concern for scaling development teams. The following table breaks down the economic impact of utilizing each model for coding tasks based on the provided test run.
| Model | Total Cost (USD) | Avg Completion Tokens | Cost/Output Token |
|---|---|---|---|
| Z.ai: GLM 5 | $0.009623 | 976 | $0.002465 |
| xAI: Grok 4 | $0.092487 | 1363 | $0.01697 |
Z.ai: GLM 5 not only outperformed xAI: Grok 4 in quality but also proved to be significantly more cost-effective, with a total cost of $0.009623 compared to $0.092487 for the same set of evaluation prompts.
Use Cases
For teams prioritizing Z.ai: GLM 5, this model is highly recommended for automated code generation, refactoring, and unit test creation where cost-per-token is a factor. xAI: Grok 4, while trailing in this specific coding evaluation, may still provide unique value in creative writing or specialized knowledge tasks outside of strictly programmatic environments.
Verdict
The Z.ai: GLM 5 vs xAI: Grok 4 comparison for Coding Performance with 10 Evaluators demonstrates a decisive advantage for GLM 5. With a score spread of 2.64, GLM 5 provides better instruction following and higher code accuracy at a fraction of the cost, making it the clear choice for production-grade coding assistance.