GLM 5.3 Batch
Neural Network
GLM 5.3 Batch is a Z.ai reasoning model with 1M token context: GLM 5.3 for code, debugging, and agent tasks.
Specifications
- Input
- text, documents
- Output
- text
- Context size
- 1 048 576 tokens
- Max answer length
- 131 072 tokens
- Reasoning effort
- low, high, max
- Capabilities
- reasoning, function calling
Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GLM 5.3 Batch?
GLM 5.3 Batch is a large reasoning model from Z.ai designed for complex development and long agent scenarios. It accepts and returns text, and the 1M token window allows you to keep an entire repository, documentation, and chat history in context. Batch mode is suitable where processing a large volume of tasks is more important than an instant response. In BotHub, the model works without a VPN or foreign card — you pay with a Russian card in rubles and only for tokens actually used, which do not expire. Other neural networks are available in the same window: you can compare responses to the same prompt, and a unified OpenAI-compatible API allows switching models without rewriting integrations. Requests are encrypted, and companies have access to contracts, invoices, EDI, and an admin panel with limits. Simple scenarios: analyzing legacy code and refactoring large modules, finding bug causes via logs and source code, agents managing tasks from setup to solution, batch processing of task sets, and mass processing of technical documentation.Frequently asked questions about GLM 5.3 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.
glm-5.3:batch is a text model from Z.ai with a context of about a million tokens. It works with prompts and documents, reasons before answering, and calls external functions. Suitable for code, parsing large files, analytics, STEM tasks, and agent scenarios with tools.
The output limit is 131,072 tokens per response. This is enough for a voluminous document, detailed analysis, or a large code file in its entirety, without splitting. If the response hits the limit, just ask it to continue — the model will write the next fragment.
Yes, reasoning before answering is a stated capability of the model: it breaks down the task step-by-step before formulating the result. This is noticeable in math, logic, code debugging, and multi-step instructions where it is important to arrive at the answer rather than guess it.
Yes, the model supports function calling and structured JSON output. You describe the available tools, it selects the necessary one and returns arguments according to the specified schema. In BotHub, this works via a unified OpenAI-compatible API, so switching models will not require rewriting the integration.
According to our data, the model accepts text and documents — this is a confirmed scenario: upload a file with materials, and it will parse the content. Multimodal input of images, audio, or video is not noted for glm-5.3:batch, so for media in BotHub, switch to a specialized model.
We have no data on prompt and output languages, so we will not promise Russian quality — test it with your task. You can compare its response with other models in one window. The service itself is available from Russia: no VPN or foreign card needed, pay-as-you-go for tokens.