DeepSeek-R1 vs GPT-6 Astra
A direct, empirical evaluation of DeepSeek-R1 (DeepSeek-AI) against GPT-6 Astra (OpenAI).
DeepSeek-R1
Developed by DeepSeek-AI · 671B parameters
Olympiad mathematics, logic verification, and autonomous thinking chains.
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-R1 | GPT-6 Astra |
|---|---|---|
| SWE-bench Verified | 49.2% | 79.4% |
| MMLU-Pro | 84% | 91.2% |
| MATH-500 | 97.3% | 98.6% |
| Context Window | 128,000 tokens | 1,050,000 tokens |
| Input Pricing (per 1M) | $0.55 | $10.00 |
| Output Pricing (per 1M) | $2.19 | $50.00 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-R1 (DeepSeek-AI) alongside GPT-6 Astra (OpenAI) highlights key technical trade-offs across neural parameter scale and compute efficiency. DeepSeek-R1 executes across 671B total parameters with 37B 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-R1 achieves 49.2% autonomous resolution rate, compared to 79.4% for GPT-6 Astra. On complex reasoning benchmarks, DeepSeek-R1 posts 84% on MMLU-Pro and 97.3% 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 $10.42 on DeepSeek-R1 versus $220.00 on GPT-6 Astra.
Recommendation: Deploy DeepSeek-R1 when your infrastructure prioritizes olympiad mathematics, logic verification, and autonomous thinking chains.. Deploy GPT-6 Astra when workload requirements favor complex multi-step engineering, formal math proofs, autonomous computer/browser navigation, and cad synthesis..