Overview
In this technical evaluation, we compare OpenAI: GPT-6 Astra and DeepSeek: DeepSeek V4.1 Flash to assess their relative coding performance. Utilizing PeerLM's comparative ranking framework, 9 independent judge models evaluated the responses generated by both candidates. This analysis focused on Coding Performance with 10 Evaluators, utilizing a sample size of 4 test cases and 4 evaluated responses per model to determine a relative preference score.
Benchmark Results
The evaluation utilized a relative preference methodology, where judges ranked outputs against one another, with these rankings mapped to a 0-10 scale. It is important to note that these scores reflect judge preference rather than verified execution or ground-truth correctness.
| Model | Overall Score | Avg Latency (ms) | Total Cost (USD) |
|---|---|---|---|
| DeepSeek: DeepSeek V4.1 Flash | 7.94 | 721 | 0.004445 |
| OpenAI: GPT-6 Astra | 2.06 | 534 | 0.0475 |
Criteria Breakdown
The models were evaluated on their ability to generate high-quality code responses. Judges were tasked with ranking the responses based on their overall utility and alignment with the coding prompts. The resulting scores represent the relative standing of each model within this specific test set, rather than an absolute accuracy metric.
Cost & Latency
When considering the deployment of these models, the trade-off between latency and cost is a significant factor. OpenAI: GPT-6 Astra demonstrated lower average latency at 534ms compared to DeepSeek: DeepSeek V4.1 Flash at 721ms. However, DeepSeek: DeepSeek V4.1 Flash proved significantly more cost-effective for these coding tasks, with a total cost of $0.004445 across the evaluated test cases compared to $0.0475 for OpenAI: GPT-6 Astra.
Use Cases
Given the specific focus on Coding Performance with 10 Evaluators, these models may be considered for tasks requiring code generation or synthesis. The current data suggests that for the specific prompts used in this run, DeepSeek: DeepSeek V4.1 Flash was more frequently favored by the judge models. Users should weigh the higher latency of the DeepSeek model against its superior relative ranking and cost efficiency.
Limitations
This evaluation is based on a limited sample of 4 test cases and 4 responses per model. The findings reflect relative judge preference on these specific examples and do not assess correctness, nor do they reflect performance across broader coding domains. This run did not test for tool-use capabilities, execution accuracy, or production-grade stability.
Verdict
On these specific examples, DeepSeek: DeepSeek V4.1 Flash received a higher relative preference score from the evaluators compared to OpenAI: GPT-6 Astra. While OpenAI: GPT-6 Astra offers lower latency, the performance spread observed in this evaluation highlights a notable preference for the outputs generated by DeepSeek: DeepSeek V4.1 Flash.