GLM 5.3 Flash
Neural Network
GLM 5.3 Flash is a natively multimodal Z.ai model for coding, agentic tasks, and API usage.
Max answer length
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Context size
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Prompt cost
(per 1M tokens)
Answer cost
(per 1M tokens)
How it works GLM 5.3 Flash?
GLM 5.3 Flash is a natively multimodal model from Z.ai, focused on cost-effective coding and long agentic scenarios. Its hybrid attention architecture, combining sparse and linear mechanisms, maintains accuracy over large context volumes, ensuring long step chains are completed without losing the thread of reasoning. In BotHub, GLM 5.3 Flash works from Russia without a VPN or foreign card: pay in rubles with a Russian card only for tokens actually used, with no expiration on unused balances. Access over 250 models in the same window with easy switching, a unified OpenAI-compatible API, AES-GCM encryption, and corporate access via contract with invoicing and an admin panel. Developers can use it for writing and refactoring code, analyzing large repositories, and debugging. Agent builders can use it for multi-step pipelines, analysts for working with voluminous documents, and product teams to build a multimodal service on a single key, switching models per task without rewriting integrations.Frequently asked questions about GLM 5.3 Flash
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 a context window of 1,048,576 tokens. It accepts text, images, and documents as input, reasons before answering, and calls functions. It is suitable for working with large codebases, analyzing voluminous documents, analytics, and tasks requiring long context.
The output limit is 943,717 tokens per response, so the model can easily output voluminous documentation, long reports, or large code files in their entirety. You likely won't need to split tasks into parts and manually glue fragments together.
Yes, the reasoning mode is confirmed by our data: before the final answer, the model runs an internal chain of thought. This helps with math, logic, code debugging, and multi-step tasks where it is important not just to guess the result, but to arrive at it sequentially.
Yes, both function calling and structured JSON output are supported. You can connect the model to your tools, databases, and external services, and receive answers in a predictable schema. This is convenient for agents, pipelines, and automation without hacks for parsing raw text.
Images and documents, yes, they are stated as supported input types: send a screenshot, diagram, table, or file and ask for analysis. There is no mention of audio or video in our data, so you shouldn't count on them in advance.
There is no specific note about languages in our data, so we won't make promises for the model — test it on your real task, it will take a couple of minutes. What is known for sure: in BotHub, it is available from Russia without a VPN or foreign card, with payment in rubles.