GLM 5 Turbo
Neural Network
GLM 5 Turbo is a Z.ai model for agent scenarios and fast responses; the GLM 5 Turbo API is suitable for workflow automation.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 202 752 tokens
- Max answer length
- 131 072 tokens
- Reasoning effort
- low, medium, high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GLM 5 Turbo?
GLM 5 Turbo is a Z.ai model designed for fast response and reliable performance in agent environments. It is optimized for real-world agent workflows that require not just answering, but executing a chain of steps and accessing external tools, making it well-suited for latency-sensitive automation scenarios. In BotHub, access is available without a VPN or foreign card: you pay with a Russian card in rubles for tokens as you use them, and they do not expire. In the same window, there are over 250 neural networks to easily switch between and compare answers, and a unified OpenAI-compatible API allows you to change models in a multi-model system without rewriting the integration; chats are encrypted via AES-GCM, and for companies, there is a contract, invoice, EDI, and an admin panel with limits. Use GLM 5 Turbo for agents that navigate tools to complete tasks, for automating routine support and internal services, for streaming request processing where speed is critical, and for prototyping multi-agent pipelines.Frequently asked questions about GLM 5 Turbo
You can use generated results for commercial purposes. You own all rights to the content you create. The only restriction: make sure your prompt does not include copyrighted third-party material. You are responsible for respecting the rights to any input data.
This is a text model from Z.ai with reasoning, function calling, and image/document input support. It is suitable for coding tasks: writing, refactoring, debugging, as well as analyzing long documents, analytics, STEM tasks, and embedding into multi-agent scenarios via the unified OpenAI-compatible BotHub API.
The output limit is 131,072 tokens per response with a context window of 202,752 tokens. This is enough for a large technical document, a voluminous code module, or a detailed analysis in its entirety, without splitting the task into dozens of separate requests and manually stitching parts together.
Yes, reasoning before answering is confirmed by our data. The model first outlines the solution path and then provides the result — this is noticeable in math, logic, multi-step analysis, and code debugging. The benefit is smaller for simple queries, but for complex ones, the answer is more coherent.
Yes, both function calling and structured JSON output are supported. Describe your tools — the model will select the right one, pass arguments, and return the answer according to the specified schema. Convenient for agents, document parsing, and integrations with your backend via a unified API.
The model accepts images and documents as input: it will analyze a screenshot, diagram, table, or file and respond with text. There is no mention of audio or video in our data, so for voiceovers or analyzing recordings, it is more convenient to use a specialized model in the same BotHub window.
There is no specific note about languages in our data, so we won't make any promises. It is easier to test it on your own task: send a typical prompt in BotHub, compare the output with another model in the same window, and keep the one that performed better. Switching does not require rewriting the integration.