DeepSeek-V4.1-Flash-1017 vs GLM-5-3 (Flagship)
A direct, empirical evaluation of DeepSeek-V4.1-Flash-1017 (DeepSeek) against GLM-5-3 (Flagship) (Zhipu AI & THUDM).
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.
GLM-5-3 (Flagship)
Developed by Zhipu AI & THUDM · 744B parameters
Complex algorithmic reasoning, multi-step code synthesis, and long-horizon tool execution.
| Metric | DeepSeek-V4.1-Flash-1017 | GLM-5-3 (Flagship) |
|---|---|---|
| SWE-bench Verified | 81.6% | 54.8% |
| MMLU-Pro | 92.8% | 78.4% |
| MATH-500 | 94.8% | 93.6% |
| Context Window | 128,000 tokens | 256,000 tokens |
| Input Pricing (per 1M) | $0.14 | $0.55 |
| Output Pricing (per 1M) | $0.28 | $2.19 |
Architectural & Practical Deployment Analysis
Evaluating DeepSeek-V4.1-Flash-1017 (DeepSeek) alongside GLM-5-3 (Flagship) (Zhipu AI & THUDM) 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 GLM-5-3 (Flagship) operates on 744B parameters with 44B active per token.
On SWE-bench Verified software engineering tasks, DeepSeek-V4.1-Flash-1017 achieves 81.6% autonomous resolution rate, compared to 54.8% for GLM-5-3 (Flagship). On complex reasoning benchmarks, DeepSeek-V4.1-Flash-1017 posts 92.8% on MMLU-Pro and 94.8% on MATH-500, against 78.4% and 93.6% respectively for GLM-5-3 (Flagship).
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 $10.42 on GLM-5-3 (Flagship).
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 GLM-5-3 (Flagship) when workload requirements favor complex algorithmic reasoning, multi-step code synthesis, and long-horizon tool execution..