DeepSeek-V4.1-Flash-1017 vs Qwen 2.5 Max
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against Qwen 2.5 Max (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 2.5 Max
Developed by Alibaba Cloud · Proprietary (~700B) parameters
Cross-lingual document extraction and structured structured output generation.
| Metric | DeepSeek-V4.1-Flash-1017 | Qwen 2.5 Max |
|---|---|---|
| SWE-bench Verified | 81.6% | 51.4% |
| MMLU-Pro | 92.8% | 77.2% |
| MATH-500 | 94.8% | 89.4% |
| Context Window | 128,000 tokens | 128,000 tokens |
| Input Pricing (per 1M) | $0.14 | $1.60 |
| Output Pricing (per 1M) | $0.28 | $6.40 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside Qwen 2.5 Max (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 2.5 Max operates on Proprietary (~700B) parameters with Dense/MoE Hybrid active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 51.4% for Qwen 2.5 Max. On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 77.2% and 89.4% respectively for Qwen 2.5 Max.
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 $30.40 on Qwen 2.5 Max.
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 2.5 Max when workload requirements favor cross-lingual document extraction and structured structured output generation..