Overview
As LLM capabilities evolve, developers require clear insights into how models compare across specific technical tasks. This report presents a comparative analysis of Anthropic: Claude Fable 5.1 vs OpenAI: GPT-6 Astra, specifically focused on Coding Performance with 10 Evaluators. Our evaluation utilizes a relative preference methodology, where 9 independent judge models evaluated and ranked the outputs of both contenders.
This study is based on a controlled test environment consisting of 4 unique programming tasks. To ensure statistical visibility, each model generated 4 responses per task, resulting in a total of 35 individual judgment points. The results represent how these models were ranked relative to one another by the judge panel.
Benchmark Results
The leaderboard below summarizes the relative ranking performance of the models based on the aggregate preference scores provided by the judge panel.
| Model | Overall Score (0-10) | Avg Latency (ms) | Total Cost (USD) |
|---|---|---|---|
| Anthropic: Claude Fable 5.1 | 7.14 | 2313 | 0.17377 |
| OpenAI: GPT-6 Astra | 2.86 | 534 | 0.0475 |
Criteria Breakdown
It is important to note that the scores provided are based on a relative preference ranking. The judge models were asked to weigh the outputs based on general coding utility and performance. In this specific evaluation, the ranking reflects a unified preference rather than independent scores for accuracy or instruction following. The models were evaluated as a whole, meaning the score represents the models' ability to satisfy the judges' comparative expectations for the provided coding prompts.
Cost & Latency
Performance in coding tasks often involves a trade-off between depth of output and response time. Anthropic: Claude Fable 5.1 demonstrated a higher preference score from the judges, though it operates at a higher latency of 2313ms on average compared to OpenAI: GPT-6 Astra's 534ms. Developers prioritizing speed may find the latency profile of GPT-6 Astra notable, while those seeking higher-ranked coding outputs may lean toward the performance profile of Claude Fable 5.1.
Use Cases
The scope of this evaluation was limited to Coding Performance with 10 Evaluators. These results are most applicable to developers looking for comparative model rankings in code generation tasks. Because the judges focused on relative preference, the data is best used to understand which model was more likely to be ranked higher by a panel of peers in a direct side-by-side comparison of coding output.
Limitations
This evaluation is based on a small sample size of 4 test cases. While the use of 9 judge models provides a robust set of perspectives, the results should be viewed as directional. This study did not verify code execution, test output against ground truth, or assess performance for production-scale engineering. The preference scores reflect the subjective rankings of the judge models rather than absolute correctness.
Verdict
In the direct comparison of Anthropic: Claude Fable 5.1 vs OpenAI: GPT-6 Astra, Claude Fable 5.1 achieved a higher preference ranking from our judge panel on the coding tasks provided. While GPT-6 Astra offers significantly lower latency, the judge panel consistently favored the outputs of Claude Fable 5.1 in these specific test cases.