GLM 5.3

GLM 5.3 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering, and complex multi-step automation. Reasoning efforts high and xhigh are supported; xhigh maps to max reasoning. It is particularly strong at coding and tool use across long-running tasks, able to maintain engineering context and follow standards consistently through a full development workflow, from requirements to multi-platform deployment, in a single task.

GLM 5.3

Reasoning efforts high and xhigh are supported; xhigh maps to max reasoning. It is particularly strong at coding and tool use across long-running tasks, able to maintain engineering context and follow standards consistently through a full development workflow, from requirements to multi-platform deployment, in a single task.

Base URL

https://api.icreat.ai/llm/openai/v1

Authentication

All API requests must be authenticated with an API Key. You can obtain an API Key from the console.

export ICREAT_API_KEY="your-api-key-here"

HTTP Request Headers

import os

API_KEY = os.environ.get("ICREAT_API_KEY")
headers = {
    "Content-Type": "application/json",
    "Authorization": "Bearer " + API_KEY,
}

Protect your API Key

Never expose your API Key in client-side code or public repositories. Use environment variables or a backend proxy.

Code Examples

This model is invoked via the OpenAI-compatible Chat Completions API and supports both streaming and non-streaming modes. With stream: false (default), the server returns the full JSON response at once. With stream: true, partial deltas are pushed as Server-Sent Events (SSE).

POST/chat/completions

curl --fail-with-body --connect-timeout 10 --max-time 60 \
  -X POST https://api.icreat.ai/llm/openai/v1/chat/completions \
  -H "Authorization: Bearer ${ICREAT_API_KEY}" \
  -H "Content-Type: application/json" \
  -d '{
  "model": "glm-5.3",
  "messages": [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Coffee or tea ? Which is healthier? "}
  ],
  "thinking": {"type": "enabled"},
  "reasoning_effort": "low",
  "stream": false
}'

Input Schema

The following parameters are accepted in the request body.

Total: 6 Required: 2 Optional: 4

modelstringrequired

The model ID for the completion. Must be the iCreat model_code (glm-5.3).

Example: "glm-5.3"

messagesarray[object]required

Conversation messages.

max_tokensinteger

Maximum number of tokens to generate.

temperaturenumber

Sampling temperature, 0–2.

streamboolean

If true, stream via Server-Sent Events.

thinkingobject

Extended thinking configuration (if supported).

Output Schema

OpenAI-compatible Chat Completions response.

Total: 6

idstring

Unique completion identifier.

objectstring

Object type, always chat.completion.

createdinteger

Unix timestamp.

modelstring

Model ID used.

choicesarray[object]

List of completion choices.

usageobject

Token usage statistics.

LLM-friendly prompt

Below is an LLM-friendly Markdown prompt you can copy into Cursor, ChatGPT, or other AI assistants to help them understand this model's API integration, call flow, and key parameters.

# glm-5.3

> GLM 5.3 is a large-scale reasoning model from Z.ai.

## Overview

Call via iCreat OpenAI-compatible Chat Completions API; supports streaming and non-streaming and extended thinking.

## API Info

- **Base URL**: `https://api.icreat.ai/llm/openai/v1`
- **Endpoint (POST)**: `/chat/completions`
- **Model ID**: `glm-5.3`
- **Auth**: `Authorization: Bearer ${ICREAT_API_KEY}`

## Call Flow

Single POST; `stream: false` returns full JSON, `stream: true` streams via SSE.

### Input

- `model` (required): iCreat model_code `glm-5.3`
- `messages` (required): conversation messages
- Common optional: `max_tokens`, `temperature`, `stream`, `thinking`

### Output

- Read reply from `choices[0].message.content`

## Notes

- `model` must be the iCreat model_code
- Other fields follow the OpenAI Chat Completions protocol