GLM 5.3 Flash

Supported Functionality

Item	Specification
Input	Text, images, video, and files
Output	Text
Context	1,000,000 tokens
Max Output	128,000 tokens
Vision	✓ Supported
Function Calling	✓ Supported

Description

GLM-5.3-Flash is Z.ai's first natively multimodal model in the GLM-5 family, with the API code glm-5.3-flash. Its mixture-of-experts architecture contains 320B total parameters while activating 18B per token. It accepts text, images, video, and files, produces text, and provides a one-million-token context window with up to 128,000 output tokens.

The model is designed for efficient coding, long-horizon agents, and visually grounded execution loops. It can coordinate work across code, browsers, and graphical interfaces, then use rendered or interactive feedback to inspect and improve its output. It also supports always-on thinking, low, high, and max reasoning_effort, streaming, function calling, context caching, and structured output. Z.ai separately offers glm-5.3-flashx, rated at up to 200 tokens per second, and the two variants use distinct model codes.

Key Capabilities

• Native Multimodal Understanding: Processes text, images, video, and files together for screenshots, visual documents, interface states, and long-form footage.
• Vision-Driven Coding: Understands layouts, design systems, and interaction logic from references, then uses rendered output to inspect and refine implementations.
• Long-Horizon Agent Execution: Decomposes goals, invokes tools, reads environmental feedback, and continues multi-stage workflows through repeated validation.
• Project-Level Code Generation: Handles frontend, game, automation, and cross-file engineering tasks beyond isolated code completion.
• Professional File Workflows: Works with tools to create, inspect, and iterate on deliverables such as PPTX, PDF, DOCX, and XLSX files.
• Spatial and 3D Work: Interprets spatial relationships from descriptions, sketches, or references for Blender scenes, CAD models, and game prototypes.
• Deep Reasoning and Tool Collaboration: Combines tiered reasoning, function calling, and structured output with external systems to form verifiable execution loops.

Technical Strengths

Feature	Benefit
Sparsely Activated MoE	Activates only 18B of 320B total parameters per token, improving inference efficiency while retaining strong capability.
Hybrid Sparse and Linear Attention	Uses linear attention for local dependencies and sparse attention for global retrieval across million-token contexts.
IndexPool and mHC	IndexPool compresses index vectors, while Manifold-Constrained Hyper-Connections improve scaling and information flow.
30T-Token Multimodal Pretraining	A large joint text-and-vision corpus supports interface understanding, visual coding, and professional document work.
Adjustable Reasoning Effort	low, high, and max levels let applications align reasoning depth with task difficulty and interaction needs.
Open-Weight Serving Ecosystem	Official MIT-licensed weights are available with deployment paths for Transformers, vLLM, SGLang, and other frameworks.

Frequently Asked Questions

What is the difference between GLM-5.3-Flash and GLM-5.3?

GLM-5.3-Flash is natively multimodal, directly accepts images, video, and files, and uses a smaller active parameter count for frequent coding and visual-agent workflows. GLM-5.3 focuses more heavily on difficult text-based engineering and complex long-horizon work; compare them on the same production tasks for completion quality, response time, and tool reliability.

How much context and output does GLM-5.3-Flash support?

The official documentation specifies a one-million-token context window and up to 128,000 output tokens. The practical input budget also depends on reserved output, image or video encoding, tool definitions, and conversation history, so long-running agents still need context management.

When should I use low, high, or max reasoning effort with GLM-5.3-Flash?

Evaluate low for classification, extraction, and short interactions, use high for complex analysis and multi-step tool work, and test max for demanding engineering or long-horizon agents. Z.ai recommends reasoning_effort: max, but production selection should be based on success rate, response time, and tool behavior in your own evaluation set.

How do I call GLM-5.3-Flash on LinkModel?

GLM-5.3-Flash is available on LinkModel. Copy the public model ID from LinkModel's live model page and follow that page's current request endpoint, authentication method, message structure, and multimodal fields; do not assume the official Z.ai model code is also the LinkModel ID.

How should I validate the GLM-5.3-Flash integration on LinkModel?

Start with a minimal text request to confirm the model ID, streaming events, and error structure, then test images, long context, structured output, and tool calls separately. Use real business samples to verify reasoning-content handling, tool argument parsing, retries, and confirmation gates for high-impact actions, following the capability boundaries on LinkModel's current model page.