DeepSeek-V4.1-Flash-1017 vs Qwen 4 Max (Leaked Staging)
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against Qwen 4 Max (Leaked Staging) (Alibaba Cloud).
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.
Qwen 4 Max (Leaked Staging)
Developed by Alibaba Cloud · 1.2 Trillion (Sparse MoE) parameters
Repository-scale software engineering outscoring GPT-6 Sol, multilingual enterprise agents across 120 languages, and low-cost 1M-token processing.
| Metric | DeepSeek-V4.1-Flash-1017 | Qwen 4 Max (Leaked Staging) |
|---|---|---|
| SWE-bench Verified | 81.6% | 78.6% |
| MMLU-Pro | 92.8% | 91.4% |
| MATH-500 | 94.8% | 95.6% |
| Context Window | 128,000 tokens | 1,000,000 tokens |
| Input Pricing (per 1M) | $0.14 | $1.20 |
| Output Pricing (per 1M) | $0.28 | $4.80 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside Qwen 4 Max (Leaked Staging) (Alibaba Cloud) 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 Qwen 4 Max (Leaked Staging) operates on 1.2 Trillion (Sparse MoE) parameters with 96 Billion Active Parameters active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 78.6% for Qwen 4 Max (Leaked Staging). On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 91.4% and 95.6% respectively for Qwen 4 Max (Leaked Staging).
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 $22.80 on Qwen 4 Max (Leaked Staging).
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 Qwen 4 Max (Leaked Staging) when workload requirements favor repository-scale software engineering outscoring gpt-6 sol, multilingual enterprise agents across 120 languages, and low-cost 1m-token processing..