DeepSeek-V4.1-Flash-1017 vs GPT-6 Luna
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against GPT-6 Luna (OpenAI).
DeepSeek-V4.1-Flash-1017
Developed by DeepSeek · Open-Weight Distilled Transformer (MIT License) parameters
Top-tier open-weight coding (81.6% SWE-bench Verified), concise reasoning traces with 32% lower token bloat, and fast consumer GPU self-hosting.
GPT-6 Luna
Developed by OpenAI · Proprietary High-Throughput Distillation parameters
High-throughput document extraction, sub-second routing, customer service triage, code autocomplete, and cost-optimized reasoning via Luna Pro mode.
| Metric | DeepSeek-V4.1-Flash-1017 | GPT-6 Luna |
|---|---|---|
| SWE-bench Verified | 81.6% | 48.2% |
| MMLU-Pro | 92.8% | 74.6% |
| MATH-500 | 94.8% | 84.1% |
| Context Window | 128,000 tokens | 1,050,000 tokens |
| Input Pricing (per 1M) | $0.14 | $0.10 |
| Output Pricing (per 1M) | $0.28 | $0.50 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside GPT-6 Luna (OpenAI) highlights key technical trade-offs across neural parameter scale and compute efficiency. DeepSeek-V4.1-Flash-1017 executes across Open-Weight Distilled Transformer (MIT License) total parameters with Multi-Head Latent Attention (MLA) Core active per token, whereas GPT-6 Luna operates on Proprietary High-Throughput Distillation parameters with Fast Utility & Speculative Decoder active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 48.2% for GPT-6 Luna. On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 74.6% and 84.1% respectively for GPT-6 Luna.
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $1.82 on DeepSeek-V4.1-Flash-1017 versus $2.20 on GPT-6 Luna.
Recommendation: Deploy DeepSeek-V4.1-Flash-1017 when your infrastructure prioritizes top-tier open-weight coding (81.6% swe-bench verified), concise reasoning traces with 32% lower token bloat, and fast consumer gpu self-hosting.. Deploy GPT-6 Luna when workload requirements favor high-throughput document extraction, sub-second routing, customer service triage, code autocomplete, and cost-optimized reasoning via luna pro mode..