DeepSeek V4.1 Flash vs DeepSeek-V4.1-Flash-1017
A direct, empirical evaluation of DeepSeek V4.1 Flash (DeepSeek-AI) against DeepSeek-V4.1-Flash-1017 (DeepSeek).
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.
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 V4.1 Flash | DeepSeek-V4.1-Flash-1017 |
|---|---|---|
| SWE-bench Verified | 67.2% | 81.6% |
| MMLU-Pro | 78.4% | 92.8% |
| MATH-500 | 92.6% | 94.8% |
| Context Window | 1,000,000 tokens | 128,000 tokens |
| Input Pricing (per 1M) | $0.06 | $0.14 |
| Output Pricing (per 1M) | $0.22 | $0.28 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek V4.1 Flash (DeepSeek-AI) alongside DeepSeek-V4.1-Flash-1017 (DeepSeek) highlights key technical trade-offs across neural parameter scale and compute efficiency. DeepSeek V4.1 Flash executes across 552B total parameters with 8B input / 16B output 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 V4.1 Flash achieves 67.2% autonomous resolution rate, compared to 81.6% for DeepSeek-V4.1-Flash-1017. On complex reasoning benchmarks, DeepSeek V4.1 Flash posts 78.4% on MMLU-Pro and 92.6% 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 $1.04 on DeepSeek V4.1 Flash versus $1.82 on DeepSeek-V4.1-Flash-1017.
Recommendation: Deploy DeepSeek V4.1 Flash when your infrastructure prioritizes high-throughput software engineering, autonomous coding loops, terminal agents, 1m long-context retrieval, and low-cost production inference.. 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..