Overview
This report provides a comparative analysis of two prominent large language models, Anthropic: Claude Fable 5.1 and Qwen: Qwen3.8 Max (0902), focusing specifically on their Coding Performance with 10 Evaluators. The evaluation was conducted using PeerLM's comparative framework, which utilizes relative preference ranking rather than static rubric scoring. By analyzing model responses side-by-side, we captured the nuanced preferences of 9 independent judge models across 4 distinct coding test cases.
Benchmark Results
In this evaluation, each model generated 4 responses, resulting in a total of 36 individual judgments provided by the judge panel. The scoring represents the relative preference rank mapped onto a 0-10 scale, indicating how often a model's output was favored by the judges during the evaluation process.
| Model | Overall Score | Avg Latency (ms) | Total Cost (USD) |
|---|---|---|---|
| Anthropic: Claude Fable 5.1 | 5.28 | 2879 | 0.15854 |
| Qwen: Qwen3.8 Max (0902) | 4.72 | 3754 | 0.021068 |
Criteria Breakdown
The evaluation centered on a holistic assessment of coding output. It is important to note that the scores presented here reflect the aggregate relative preference of the judge models. Because this was a comparative ranking exercise, the scores represent the models' ability to satisfy the judges' expectations relative to one another within the scope of Coding Performance with 10 Evaluators.
Cost & Latency
Performance in a coding environment often necessitates a balance between speed and cost. Anthropic: Claude Fable 5.1 demonstrated a faster average latency of 2879ms compared to the 3754ms recorded for Qwen: Qwen3.8 Max (0902). However, Qwen: Qwen3.8 Max (0902) proved significantly more cost-efficient for these tasks, with a total cost of 0.021068 USD compared to 0.15854 USD for Claude Fable 5.1. Developers should weigh these latency and budget considerations based on their specific integration requirements.
Use Cases
The models were tested on their ability to generate code snippets and follow specific programming instructions. These results are most applicable to developers seeking to understand how these models perform in a comparative setting. Given the nature of the evaluation, these models are best suited for tasks where coding assistance is required, though users should perform their own validation before deploying generated code in sensitive environments.
Limitations
This evaluation is based on a limited sample size of 4 test cases per model, resulting in 4 responses per model. These findings are directional and represent a snapshot of relative performance in a specific task scope. This evaluation did not verify code execution, check for logical correctness against ground truth, or assess performance in production-grade software engineering pipelines.
Verdict
Based on the relative preference rankings, Anthropic: Claude Fable 5.1 outperformed Qwen: Qwen3.8 Max (0902) on these specific examples, achieving a higher aggregate score. While Claude Fable 5.1 offers lower latency, Qwen: Qwen3.8 Max (0902) provides a more economical option for similar coding tasks. Users should consider these results as a comparative baseline rather than an absolute measure of model quality.