GLM-5.2 by Z.ai is a 744B open-weight (MIT) mixture-of-experts coding model with a 1M-token context, IndexShare sparse attention, and selectable thinking-effort levels.
Available on
Privacy
- Zero data retention
- Always-on
- Training
- No
- Region
- EU + US
Voidleap Code is a desktop agentic IDE from Voidleap that runs on your own machine, with every agent, skill, hook, and security rule left editable. You can change model in the middle of a thread, planning on one, implementing on another, and reviewing on a third without starting over, then read back the tokens, cost, latency, and cache hit rate for each. Opper ships inside the app as a provider rather than a base URL override, so you add your key once in settings and pick Opper models per turn.
In Voidleap Code, go to Settings → Providers → Add Provider, choose Opper, and paste your API key. Then pick GLM-5.2.
GLM-5.2 runs on the routes below. Route only to EU providers when your data has to stay in Europe.
| Provider | Region | Zero data retention | Training | Input | Output |
|---|---|---|---|---|---|
| Berget | EU | Zero data retention | No | $1.63 | $5.11 |
| evroc | EU | Zero data retention | No | $1.45 | $5.81 |
| Inceptron | EU | Zero data retention | No | $0.75 | $2.90 |
| Melious | EU | Zero data retention | No | $1.16 | $4.65 |
| Nebius | EU | Not by default | No | $1.40 | $4.40 |
| Regolo | EU | Zero data retention | No | $2.32 | $6.04 |
| Sference | EU | Zero data retention | No | $1.20 | $4.20 |
| TensorX | EU | Zero data retention | No | $1.50 | $4.50 |
| DeepInfra | US | Zero data retention | No | $0.75 | $2.40 |
| DigitalOcean | US | Not by default | No | $0.70 | $2.20 |
| Morph | US | Not by default | No | $1.10 | $4.10 |
| Novita | US | Zero data retention | No | $1.40 | $4.40 |
| Wafer | US | Zero data retention | No | $1.26 | $3.96 |
| Alibaba Cloud | Multi | Not by default | No | $1.19 | $4.17 |
| BytePlus | Multi | Not by default | No | $1.40 | $4.40 |
| Fireworks | Multi | Zero data retention | No | $1.40 | $4.40 |
GLM-5.2 by Z.ai is a 744B open-weight (MIT) mixture-of-experts coding model with a 1M-token context, IndexShare sparse attention, and selectable thinking-effort levels.
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