Overview
In this evaluation, we assess the comparative performance of two prominent language models: Anthropic: Claude Fable 5.1 and DeepSeek: DeepSeek V4.1 Flash. The focus of this analysis is strictly limited to Coding Performance with 10 Evaluators. This assessment utilized a comparative methodology where 9 judge models reviewed responses to 4 specific coding-related test cases, resulting in a total of 35 individual judgments.
Benchmark Results
The models were ranked based on relative preference, where judges compared outputs against one another to assign a score on a 0-10 scale. This score reflects the cumulative ranking preference rather than an absolute accuracy metric.
| Model | Overall Score | Avg Latency (ms) | Total Cost (USD) |
|---|---|---|---|
| DeepSeek: DeepSeek V4.1 Flash | 5.43 | 721 | 0.004445 |
| Anthropic: Claude Fable 5.1 | 4.57 | 2879 | 0.15854 |
Criteria Breakdown
The evaluation methodology relied on comparative ranking to determine preference. Judges evaluated the outputs based on their ability to handle coding tasks effectively. It is important to note that the scores provided (5.43 for DeepSeek V4.1 Flash and 4.57 for Claude Fable 5.1) are normalized representations of the relative preference rankings assigned by the 9 participating judge models across the 4 test cases.
Cost & Latency
When choosing between these models, operational efficiency is a key consideration. DeepSeek: DeepSeek V4.1 Flash demonstrated significantly lower latency, averaging 721ms compared to 2879ms for Anthropic: Claude Fable 5.1. Furthermore, the cost profile differs substantially, with DeepSeek: DeepSeek V4.1 Flash maintaining a much lower total cost profile for the 4 responses evaluated in this run.
Use Cases
The results of this specific suite suggest that DeepSeek: DeepSeek V4.1 Flash may be better suited for scenarios where rapid response times and cost-efficiency are prioritized within coding-related workflows. Anthropic: Claude Fable 5.1 remains an alternative, though it exhibited higher latency and cost in this specific sample of coding tasks.
Limitations
This report is based on a small sample size of 4 test cases. The findings represent the relative preferences of 9 judge models for these specific inputs. This evaluation did not involve executing code, verifying correctness against ground truth, or testing for production-level reliability. These results should be interpreted as directional indicators for coding preference in the context of this specific evaluation suite.
Verdict
On the examples provided, DeepSeek: DeepSeek V4.1 Flash outperformed Anthropic: Claude Fable 5.1 in both relative preference ranking and operational efficiency. While DeepSeek: DeepSeek V4.1 Flash achieved a higher score of 5.43, users should consider their specific latency and budget requirements when selecting a model for coding assistance tasks.