Overview
In this evaluation, we examine the relative performance of two prominent language models—Qwen: Qwen3.8 Max (0902) and DeepSeek: DeepSeek V4.1 Flash—focused specifically on coding tasks. Our methodology utilizes a comparative ranking approach where 9 independent judge models reviewed responses to 4 distinct coding prompts. By aggregating these relative preferences, we can observe how each model ranks in a head-to-head comparison.
Benchmark Results
The evaluation consisted of 4 test cases per model, resulting in a total of 34 unique judgments provided by our panel of 9 evaluators. The scores reflect the relative preference of these judges, mapped onto a 0-10 scale. This ranking-based approach allows us to see which model's output is consistently favored by peers in a controlled environment.
| Model | Rank | Overall Score (0-10) | Avg Latency (ms) | Total Cost (USD) |
|---|---|---|---|---|
| DeepSeek: DeepSeek V4.1 Flash | 1 | 8.53 | 721 | 0.004445 |
| Qwen: Qwen3.8 Max (0902) | 2 | 1.47 | 3754 | 0.021068 |
Cost & Latency
Engineering teams often balance performance with operational costs and speed. In this specific suite for Coding Performance with 10 Evaluators, the models exhibited clear differences:
- DeepSeek: DeepSeek V4.1 Flash: Demonstrated significantly lower latency, averaging 721ms per response, with a total cost of $0.004445 for the sample set.
- Qwen: Qwen3.8 Max (0902): Recorded an average latency of 3754ms per response, with a total cost of $0.021068 for the sample set.
Use Cases
The Qwen: Qwen3.8 Max (0902) vs DeepSeek: DeepSeek V4.1 Flash comparison highlights distinct profiles for developers. DeepSeek: DeepSeek V4.1 Flash appears particularly suited for workflows requiring rapid iteration and high-throughput code generation, while Qwen: Qwen3.8 Max (0902) represents an alternative in the current landscape of large language models.
Limitations
It is important to note that this evaluation is based on a small sample size of 4 test cases. These scores represent relative preference rankings from judge models and do not indicate absolute correctness, functional code execution, or performance in production-grade environments. We have not verified the generated code against ground truth or external unit tests; the results reflect only the subjective ranking of the models by the 9 participating judge models.
Verdict
On these specific examples, DeepSeek: DeepSeek V4.1 Flash ranked higher than Qwen: Qwen3.8 Max (0902) in our comparative coding evaluation. The data suggests that for the tasks included in this run, DeepSeek: DeepSeek V4.1 Flash provided responses that were more consistently preferred by the judge models while maintaining a lower latency profile.