GLM 5.3 FlashX
Neural Network
GLM 5.3 FlashX is a multimodal Z.ai model with high generation speed, suitable for chat scenarios and API integration.
Specifications
- Input
- text, images, 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 FlashX?
GLM-5.3-FlashX is a high-speed version of Z.ai's GLM-5.3-Flash: it uses the same hybrid architecture with sparse and linear attention, but focuses on output speed, reaching up to 200 tokens per second. The model is natively multimodal, accepting both text and images, with responses provided in a single dialogue. In BotHub, it is available without VPN or foreign cards: pay with a Russian card in rubles only for used tokens, with no subscription, and tokens do not expire. Nearby, in the same window, there are over 250 other neural networks to switch between, and a unified OpenAI-compatible API allows you to integrate the model into your product and switch later without rewriting the integration. Chats are encrypted via AES-GCM; for companies, we offer contracts, invoices, EDI, and an admin panel with limits. Use GLM-5.3-FlashX where fast response is critical: chatbots and first-line support, mass processing of similar requests, drafting and editing code, analyzing screenshots and images, and prototyping multimodal services.Frequently asked questions about GLM 5.3 FlashX
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 mode and tool calling. It is suitable for working with code—writing, refactoring, and debugging—as well as analytics, STEM tasks, and analyzing documents and long materials. A context of up to 1,048,576 tokens allows you to keep entire repositories and reports in the dialogue.
The output limit is 131,072 tokens per response. This is enough for a large code module, detailed technical analysis, extensive documentation, or a long analytical text without manually splitting the task into parts and stitching fragments together.
Yes, reasoning mode is supported: before the final answer, the model works through the task step-by-step. This significantly helps with math, logic, code debugging, and multi-step analytics, where a chain of reasoning is important rather than a quick, superficial result.
Yes, the model works with function calling and structured JSON output. You can connect external tools, databases, and services, build agents, and receive schema-predictable responses that go directly into your application's parser.
In addition to text, images and documents are accepted as input: you can ask it to analyze a screenshot, diagram, chart, or PDF and receive a text response. The model generates text. For audio and video, there are separate models in BotHub—switching takes a couple of seconds.
The model is multilingual and, in our tests, confidently handles Russian-language queries: explanations, document analysis, and technical tasks. The best way to evaluate quality for your task is to run a few typical queries and compare the response with another model in the same BotHub window.