DeepSeek-V4.1-Flash-1017 vs GPT-6 Astra
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against GPT-6 Astra (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 Astra
Developed by OpenAI · Proprietary Frontier parameters
Complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and CAD synthesis.
| Metric | DeepSeek-V4.1-Flash-1017 | GPT-6 Astra |
|---|---|---|
| SWE-bench Verified | 81.6% | 79.4% |
| MMLU-Pro | 92.8% | 91.2% |
| MATH-500 | 94.8% | 98.6% |
| Context Window | 128,000 tokens | 1,050,000 tokens |
| Input Pricing (per 1M) | $0.14 | $10.00 |
| Output Pricing (per 1M) | $0.28 | $50.00 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside GPT-6 Astra (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 Astra operates on Proprietary Frontier parameters with Dynamic Recurrent MoE active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 79.4% for GPT-6 Astra. On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 91.2% and 98.6% respectively for GPT-6 Astra.
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 $220.00 on GPT-6 Astra.
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 Astra when workload requirements favor complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and cad synthesis..