DeepSeek-R1 vs DeepSeek-V4.1-Flash-1017
A direct, empirical evaluation of DeepSeek-R1 (DeepSeek-AI) against DeepSeek-V4.1-Flash-1017 (DeepSeek).
DeepSeek-R1
Developed by DeepSeek-AI · 671B parameters
Olympiad mathematics, logic verification, and autonomous thinking chains.
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.
| Metric | DeepSeek-R1 | DeepSeek-V4.1-Flash-1017 |
|---|---|---|
| SWE-bench Verified | 49.2% | 81.6% |
| MMLU-Pro | 84% | 92.8% |
| MATH-500 | 97.3% | 94.8% |
| Context Window | 128,000 tokens | 128,000 tokens |
| Input Pricing (per 1M) | $0.55 | $0.14 |
| Output Pricing (per 1M) | $2.19 | $0.28 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-R1 (DeepSeek-AI) alongside DeepSeek-V4.1-Flash-1017 (DeepSeek) 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 DeepSeek-V4.1-Flash-1017 operates on Open-Weight Distilled Transformer (MIT License) parameters with Multi-Head Latent Attention (MLA) Core active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-R1 achieves 49.2% autonomous resolution rate, compared to 81.6% for DeepSeek-V4.1-Flash-1017. On complex reasoning benchmarks, DeepSeek-R1 posts 84% on MMLU-Pro and 97.3% on MATH-500, against 92.8% and 94.8% respectively for DeepSeek-V4.1-Flash-1017.
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $10.42 on DeepSeek-R1 versus $1.82 on DeepSeek-V4.1-Flash-1017.
Recommendation: Deploy DeepSeek-R1 when your infrastructure prioritizes olympiad mathematics, logic verification, and autonomous thinking chains.. Deploy DeepSeek-V4.1-Flash-1017 when workload requirements favor top-tier open-weight coding (81.6% swe-bench verified), concise reasoning traces with 32% lower token bloat, and fast consumer gpu self-hosting..