DeepSeek-R1 vs DeepSeek V4.1 Flash
A direct, empirical evaluation of DeepSeek-R1 (DeepSeek-AI) against DeepSeek V4.1 Flash (DeepSeek-AI).
DeepSeek-R1
Developed by DeepSeek-AI · 671B parameters
Olympiad mathematics, logic verification, and autonomous thinking chains.
DeepSeek V4.1 Flash
Developed by DeepSeek-AI · 552B parameters
High-throughput software engineering, autonomous coding loops, terminal agents, 1M long-context retrieval, and low-cost production inference.
| Metric | DeepSeek-R1 | DeepSeek V4.1 Flash |
|---|---|---|
| SWE-bench Verified | 49.2% | 67.2% |
| MMLU-Pro | 84% | 78.4% |
| MATH-500 | 97.3% | 92.6% |
| Context Window | 128,000 tokens | 1,000,000 tokens |
| Input Pricing (per 1M) | $0.55 | $0.06 |
| Output Pricing (per 1M) | $2.19 | $0.22 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-R1 (DeepSeek-AI) alongside DeepSeek V4.1 Flash (DeepSeek-AI) 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 operates on 552B parameters with 8B input / 16B output active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-R1 achieves 49.2% autonomous resolution rate, compared to 67.2% for DeepSeek V4.1 Flash. On complex reasoning benchmarks, DeepSeek-R1 posts 84% on MMLU-Pro and 97.3% on MATH-500, against 78.4% and 92.6% respectively for DeepSeek V4.1 Flash.
From an operational economics perspective, processing 10,000,000 mixed tokens (7M input / 3M output) costs approximately $10.42 on DeepSeek-R1 versus $1.04 on DeepSeek V4.1 Flash.
Recommendation: Deploy DeepSeek-R1 when your infrastructure prioritizes olympiad mathematics, logic verification, and autonomous thinking chains.. Deploy DeepSeek V4.1 Flash when workload requirements favor high-throughput software engineering, autonomous coding loops, terminal agents, 1m long-context retrieval, and low-cost production inference..