Overview
In this evaluation, we analyze the performance of two prominent large language models, OpenAI: GPT-6 Astra and Qwen: Qwen3.8 Max (0902), focusing specifically on their Coding Performance with 10 Evaluators. This assessment utilized a comparative ranking methodology where 9 judge models provided relative preference feedback on 4 test cases. Each model generated 4 responses, resulting in a total of 8 evaluated responses that were ranked by the judging panel to determine relative performance.
Benchmark Results
The evaluation results are based on the relative preference rankings provided by our judge panel. Scores are mapped to a 0-10 scale representing the comparative ranking of the models across the provided coding test cases. Qwen: Qwen3.8 Max (0902) achieved an overall score of 5.76, while OpenAI: GPT-6 Astra received a score of 4.24.
| Model | Overall Score | Avg Latency (ms) | Total Cost (USD) |
|---|---|---|---|
| Qwen: Qwen3.8 Max (0902) | 5.76 | 3754 | 0.021068 |
| OpenAI: GPT-6 Astra | 4.24 | 534 | 0.0475 |
Cost & Latency
Engineers often balance performance against operational overhead. In this specific run, OpenAI: GPT-6 Astra demonstrated significantly lower latency, averaging 534ms per request compared to 3754ms for Qwen: Qwen3.8 Max (0902). However, the cost profiles differ as well: Qwen: Qwen3.8 Max (0902) incurred a lower total cost of 0.021068 USD for the test set, while OpenAI: GPT-6 Astra totaled 0.0475 USD. These metrics reflect the specific task scope of Coding Performance with 10 Evaluators and may vary based on deployment scale and prompt complexity.
Use Cases
The models were evaluated exclusively on coding-related tasks. Given the relative rankings, the results highlight how each model responds to code-generation prompts within the constraints of this evaluation suite. While Qwen: Qwen3.8 Max (0902) secured a higher preference ranking from the judges, the choice of model should be informed by individual latency requirements and budget considerations per request.
Limitations
This evaluation is based on a small sample size of 4 test cases per model. The scores represent relative preference rankings from 9 judge models and do not constitute an objective measure of code correctness, security, or execution capability. This benchmark specifically measures performance for Coding Performance with 10 Evaluators and does not extrapolate to general-purpose reasoning, agentic workflows, or production-grade software engineering suitability.
Verdict
On these specific examples, Qwen: Qwen3.8 Max (0902) was ranked higher by the judge panel than OpenAI: GPT-6 Astra. While OpenAI: GPT-6 Astra offers a distinct advantage in latency for time-sensitive tasks, Qwen: Qwen3.8 Max (0902) provided a more favorable relative preference outcome within the scope of this coding-focused evaluation.