DeepSeek-V4.1-Flash-1017 vs Xiaomi MiMo-V2.6 Pro
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against Xiaomi MiMo-V2.6 Pro (Xiaomi AI Lab (Fuli Luo)).
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.
Xiaomi MiMo-V2.6 Pro
Developed by Xiaomi AI Lab (Fuli Luo) · 1.02 Trillion (Sparse MoE) parameters
High-accuracy autonomous coding (71.9% DeepSWE), cybersecurity defense (94.0% CyberGym), multimodal document reasoning, and cost-effective agentic workflows.
| Metric | DeepSeek-V4.1-Flash-1017 | Xiaomi MiMo-V2.6 Pro |
|---|---|---|
| SWE-bench Verified | 81.6% | 71.9% |
| MMLU-Pro | 92.8% | 89.2% |
| MATH-500 | 94.8% | 93.8% |
| Context Window | 128,000 tokens | 128,000 tokens |
| Input Pricing (per 1M) | $0.14 | $0.10 |
| Output Pricing (per 1M) | $0.28 | $0.20 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside Xiaomi MiMo-V2.6 Pro (Xiaomi AI Lab (Fuli Luo)) 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 Xiaomi MiMo-V2.6 Pro operates on 1.02 Trillion (Sparse MoE) parameters with 32 Billion active tokens active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 71.9% for Xiaomi MiMo-V2.6 Pro. On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 89.2% and 93.8% respectively for Xiaomi MiMo-V2.6 Pro.
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 $1.30 on Xiaomi MiMo-V2.6 Pro.
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 Xiaomi MiMo-V2.6 Pro when workload requirements favor high-accuracy autonomous coding (71.9% deepswe), cybersecurity defense (94.0% cybergym), multimodal document reasoning, and cost-effective agentic workflows..