Models
Comparison
Kimi K2 Thinking vs GLM 4.7
Kimi K2 Thinking and GLM 4.7 side by side on Silicon Network: context length, capabilities, parameters, and the per-token price of each.
| Kimi K2 Thinking | GLM 4.7 | |
|---|---|---|
| API model id | kimi-k2-thinking | glm-4.7 |
| Base model | Kimi K2 | GLM 4.7 |
| Family | Kimi | GLM |
| Parameters | 1T MoE | 355B MoE |
| Size class | Very large (200B and up) | Very large (200B and up) |
| Context length | 262,144 tokens (256K) | 202,752 tokens (198K) |
| Capabilities | Text, Reasoning, and Reasoning can be switched off | Text, Reasoning, and Reasoning can be switched off |
| Served weights | As published | 4-bit |
| Weights size | 707 GB | 199 GB |
| Input price | Unavailable | Unavailable |
| Output price | Unavailable | Unavailable |
The published rates could not be loaded just now. The pricing page reads them live.
Differences
- Kimi K2 Thinking has the longer context window: 262,144 tokens against 202,752.
- Kimi K2 Thinking is the larger model by total parameters: 1T MoE against 355B MoE.
- Both support the same capabilities: Text, Reasoning, and Reasoning can be switched off.
Whether a model is loaded on a node right now changes through the day; the pricing page shows it live. A model that is not loaded answers 404 until an operator loads it.
About these figures
Context length, capabilities, and weights size come from the model card each id is served with. Rates are the published per-token prices, refreshed every few minutes. There are no speed figures here: the network does not publish per-model throughput yet.
Silicon Network›Models›Kimi K2 Thinking vs GLM 4.713 items