Z.ai: GLM 5.3 Prime
GLM 5.3 Prime is a reasoning model from Z.ai released on Sep 23, 2026. It reads text and writes text, with a 1M token context window and up to 131K tokens per reply.
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It costs $2.80 per million input tokens and $8.80 per million output tokens. Cached input bills at $0.56, 80% less than fresh input.
It supports tool calling and prompt caching.
- Modalities
- In / out price
- $2.80 / $8.80 per 1M
- Context
- 1M
- Max output
- 131K
- Released
- Sep 23, 2026
- Knowledge cutoff
- n/a
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
GLM 5.3 Prime pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $2.80 / 1M tokens | |
|---|---|---|
| Output | $8.80 / 1M tokens | |
| Cached input (read) | $0.56 / 1M tokens | 80% off input |
What GLM 5.3 Prime costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call GLM 5.3 Prime through an OpenAI compatible API.
- Tools
tools - Reasoning
reasoning - Temperature
temperature
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GLM 5.3 Prime questions
How much does the GLM 5.3 Prime API cost?
$2.80 per million input tokens and $8.80 per million output tokens, with cached input at $0.56. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of GLM 5.3 Prime?
1,000,000 tokens of input, and it can write up to 131,072 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Does GLM 5.3 Prime support tool calling?
Yes. It accepts tool definitions, which is what an agent needs to call your APIs.
Is GLM 5.3 Prime open source?
No. Z.ai serves it through an API only, so you cannot download the weights or host it yourself.
When was GLM 5.3 Prime released?
Sep 23, 2026.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on GLM 5.3 Prime?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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