GLM-5.1
zhipuai · zhipuai/glm-5.1
Strong GLM coding model for agentic engineering, terminals, and repository generation
≈133k Chinese characters of context · Step-by-step reasoning · Calls tools / agents · Structured JSON output · Open weights, self-hostable
Read a 200,000-Chinese-character book and write a 2,000-Chinese-character summary: about $0.43
An order-of-magnitude estimate at 1.5 tokens per Chinese character and the vendor's own rate — not a quote. Full method: the glossary on the AI Models page.
Specs
| Context | 200,000(≈133k Chinese characters) |
|---|---|
| Max output | 131,072 |
| Structured output | Yes |
| Open weights | Yes |
| Released | 2026-04-07 |
Source: models.dev snapshot 2026-08-29
Who serves it, at what price
| Provider | In /M | Out /M | Cache read /M |
|---|---|---|---|
| Zhipu AIfirst-party | $1.4 | $4.4 | $0.26 |
| 302.AI | $0.86 | $3.5 | — |
| Alibaba (China) | $0.825 | $3.301 | $0.17 |
| Alibaba Token Plan | Billed by plan | Billed by plan | $0 |
| Alibaba Token Plan (China) | Billed by plan | Billed by plan | $0 |
| Auriko | $1.4 | $4.4 | $0.26 |
| Cortecs | $1.384 | $4.348 | $0.346 |
| CrofAI | $0.45 | $2.15 | $0.08 |
| DigitalOcean | $1.3 | $4.3 | $0.26 |
| DInference | $1.25 | $3.89 | — |
| GreenPT | $1.756 | $5.518 | — |
| Charm Hyper | $1.314 | $4.268 | — |
| DevPass (LLM Gateway) | $0.931 | $2.93 | $0.173 |
| Ollama Cloud | — | — | — |
| OpenCode Zen | $1.4 | $4.4 | $0.26 |
| OpenCode Go | $1.4 | $4.4 | $0.26 |
| Requesty | $1.4 | $4.4 | $0.26 |
| Z.AI | $1.4 | $4.4 | $0.26 |
| Zhipu AI Coding Plan | Billed by plan | Billed by plan | $0 |
Rows tagged “first-party” are the model vendor's own pricing; untagged rows are resale channels and may differ.
When to choose it
We have not assessed this model. It comes from the full models.dev snapshot — the specs are the vendor's published figures, and a price is the vendor's own only on rows tagged first-party; the rest are resale channels. The fit is simply not something anyone here has written.
Leaderboard
Not yet on any leaderboard we track. That does not mean the model performs poorly — it means we have not taken this snapshot for it yet, and we do not publish a score we have not measured.