Side-by-side comparison of DeepSeek V3.2 (DeepSeek · China) and GLM-4.5 (Zhipu AI · China) for self-hosted deployment of the open-weight model. DeepSeek V3.2 is rated conditional; GLM-4.5 is conditional. On the four sovereignty dimensions we track, they score identically — the difference is in context and org posture.
| Field | ||
|---|---|---|
| Summary | ||
| Verdict | Conditional 685B successor to V3 with DeepSeek Sparse Attention for long context, scalable RL for agentic tasks. Vendor claims parity with GPT-5 (Speciale variant exceeds). MIT licence keeps weights clean; Chinese-origin considerations unchanged. | Conditional MoE flagship from Zhipu under MIT. Strong agentic and coding benchmarks. Same Chinese-origin alignment and geopolitical considerations as DeepSeek / Qwen. |
| Last reviewed | 2026-04-15 | 2026-04-15 |
| Open-weight | ||
| Licence | MIT | MIT |
| Commercial use | Yes | Yes |
| Training data | Undisclosed | Undisclosed |
| Origin | China | China |
| Performance & pricing? | ||
| Quality index | 32/100 | 26/100 |
| Speed | 32 tok/s | 44 tok/s |
| Blended price | $0.32/M | $0.84/M |
| Context window | — | — |
| Evidence | ||
| Sources | ||
No overlapping sources between the two entries.