GLM 5.2 Batch
Neural Network
GLM 5.2 is a Z.ai reasoning model with 1M token context, GLM 5.2 API for agent scenarios and coding tasks.
Specifications
- Input
- text, images, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 943 718 tokens
- Reasoning effort
- none, high, extra high
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GLM 5.2 Batch?
GLM 5.2 is a large reasoning model from Z.ai: it accepts text input and output, with a 1,000,000 token context window, meaning it can handle an entire project. Its strengths lie in long agent scenarios and project-level development, while the batch version is designed for large tasks queued and processed in batches. Through BotHub, the model is accessible from Russia without a VPN or foreign card; you can pay with Russian cards in rubles, paying per token without a subscription. Over 250 models are available in the same window, making it easy to compare answers, and a unified OpenAI-compatible API allows you to switch models without rewriting integrations. Corporate teams have access to contracts, invoices, and an admin panel with limits. For developers, GLM 5.2 helps analyze unfamiliar repositories, perform multi-step refactoring, and find the root cause of intermittent bugs. For analysts, it can process long reports and documentation in one go. For those building agents, the model serves as a reasoning core for multi-step chains, and for researchers, it is useful for analyzing voluminous materials and STEM-logic tasks.Frequently asked questions about GLM 5.2 Batch
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.
The model accepts text, documents, and images, making it suitable for analyzing large files, answering questions based on documentation, coding, and analytics. A context of up to 1,048,576 tokens allows it to hold large materials in their entirety, and function calling connects it to your services. In BotHub, it is easy to compare with other models in one window.
In a single response, the model outputs up to 943,718 tokens—a very large volume that can accommodate entire documentation or a large code file. The input context reaches 1,048,576 tokens, so long materials do not need to be manually split into parts.
Yes, reasoning before answering is part of the model's capabilities. It can think through the solution process first and then provide the result—this helps with multi-step tasks, mathematics, and code analysis, where not just the output but the logic itself is important.
Yes, function calling and structured JSON output are supported. The model can be connected to your services via tools to receive answers in a machine-readable format—this is convenient for integrations, automation, and scenarios where the result is processed programmatically.
The model accepts images and documents as input: you can upload scans and pictures and ask questions about them. There is no mention of audio or video in our data, so rely on what is confirmed and check the rest with a test request.
There is no separate assessment of Russian language quality in our data, so we will not promise more than what is known. The model works with text, and it is easy to test it on your tasks: in BotHub, payment is per use, without a subscription, so you can run your own examples.