Overview
This report provides a comparative analysis of OpenAI: GPT-6 Astra vs Z.ai: GLM 5.3, centered on their coding performance. The evaluation was conducted using a PeerLM benchmarking suite designed to assess relative model preference. By utilizing 9 judge models to review the outputs, we established a ranking-based comparison for these two leading large language models.
This run specifically tested 4 unique coding-related test cases. To ensure a robust sample, each model generated 4 responses, resulting in a total of 36 individual judgments provided by our panel of evaluators. This methodology focuses on relative preference, mapping how judges ranked the models against one another on a 0-10 scale.
Benchmark Results
The following table summarizes the performance and efficiency metrics captured during the evaluation of the two models.
| Model | Overall Rank | Relative Preference Score | Avg Latency (ms) | Total Cost (USD) |
|---|---|---|---|---|
| Z.ai: GLM 5.3 | 1 | 7.5 | 473 | 0.020542 |
| OpenAI: GPT-6 Astra | 2 | 2.5 | 534 | 0.0475 |
Criteria Breakdown
The evaluation methodology employed here is based on comparative ranking. Judges were asked to assess the outputs based on general coding utility and instruction adherence. The resulting scores represent the relative preference of the judges rather than an absolute accuracy metric. It is important to note that the judges' preferences are subjective rankings and do not represent verified code execution or ground-truth validation.
Cost & Latency
Efficiency is a critical component for developers integrating LLMs into their workflows. In this specific coding evaluation, Z.ai: GLM 5.3 demonstrated a higher efficiency profile, with an average latency of 473ms compared to the 534ms observed for OpenAI: GPT-6 Astra. Furthermore, Z.ai: GLM 5.3 maintained a more favorable cost profile across the 4 test cases provided in this run.
Use Cases
The tasks focused on coding performance with 10 evaluators, covering common programming challenges. These results are intended to assist developers in understanding how these models compare when tasked with generating or refining code snippets based on the relative preferences of our judge panel.
Limitations
This evaluation is limited to 4 test cases with a total of 4 responses per model. Because the sample size is relatively small, these findings should be viewed as directional indicators of relative preference for this specific task scope. This evaluation did not test for code execution correctness, security, or performance in production environments.
Verdict
Based on the relative preference rankings in this specific evaluation, Z.ai: GLM 5.3 performed more favorably than OpenAI: GPT-6 Astra. While these results offer a valuable look at how the models stack up in a comparative coding context, developers should consider the limited sample size of 4 test cases when interpreting these scores.