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Available now on ModelsLab · Language Model

Z.ai: GLM 5 TurboAgents That Execute

Build Agentic Workflows

Fast Inference

200K Token Context

Handles long-chain tasks with 744B MoE architecture, 40B active parameters.

Tool Calling

Multi-Step Execution

Decomposes complex instructions for OpenClaw agents and persistent operations.

Reasoning Mode

High-Throughput Tasks

Supports scheduled workflows and real-time streaming in Z.ai: GLM 5 Turbo API.

Examples

See what Z.ai: GLM 5 Turbo can create

Copy any prompt below and try it yourself in the playground.

Code Review

Analyze this Python codebase for dependencies, flag cascading effects from changes, and suggest optimizations. Maintain full 200K context across turns.

Task Planner

Break down multi-step agent workflow: schedule data processing job, invoke tools for analysis, execute in OpenClaw with reasoning mode.

Bug Fixer

Fix bugs in this SWE-bench style code snippet using tool invocation and step-by-step reasoning. Output corrected code with explanations.

Architecture Map

Map dependencies in large codebase, identify architectural risks, and plan refactoring steps for Z.ai: GLM 5 Turbo model efficiency.

For Developers

A few lines of code.
Agents live. Two calls.

ModelsLab handles the infrastructure: fast inference, auto-scaling, and a developer-friendly API. No GPU management needed.

  • Serverless: scales to zero, scales to millions
  • Pay per token, no minimums
  • Python and JavaScript SDKs, plus REST API
import requests
response = requests.post(
"https://modelslab.com/api/v7/llm/chat/completions",
json={
"key": "YOUR_API_KEY",
"prompt": "",
"model_id": ""
}
)
print(response.json())

FAQ

Common questions about Z.ai: GLM 5 Turbo

Read the docs

Ready to create?

Start generating with Z.ai: GLM 5 Turbo on ModelsLab.