GLM-5.3-Flash - Zhipu's Open-Source Native Multimodal Model, aka Ox Alpha

What is GLM-5.3-Flash

GLM-5.3-Flash is Zhipu's latest open-source large model, with 32 billion total parameters and only 1.8 billion activated parameters, making it the first native multimodal model in the GLM-5 series. The model achieves a comprehensive intelligence index of 57 points, on par with Claude Opus 4.8 , while priced at only one-fortieth of it. The model adopts a hybrid sparse and linear attention architecture and manifold-constrained hyper-connections technology, supports visual encoding, code generation, and professional document processing, and for the first time achieves large-scale efficient deployment on domestic chip clusters, improving end-to-end performance threefold.

Main Features of GLM-5.3-Flash

Native Multimodal Understanding: Supports joint understanding and generation of text and images, and is the first native multimodal model in the GLM-5 series.
Visual Encoding: Natively integrates visual capabilities into the model, enabling it to autonomously determine when "observation" is needed and use visual feedback to guide programming and actions.
Code Generation and Iteration: Supports development tasks such as front-end, games, and 3D simulation, and can self-verify and iteratively improve code based on rendering and interaction feedback.
Professional Document Processing: Optimized for Office documents such as PPTX, PDF, DOCX, and XLSX, with aesthetic judgment and self-verification capabilities.
Financial and Legal Work: Covers professional scenarios such as financial research report generation, modeling and analysis, as well as contract review and legal document drafting.
Agent Collaboration: Enables collaborative operations across code, browsers, and graphical interfaces through Browser Use Agent and Computer Use Agent.

Technical Principles of GLM-5.3-Flash

Hybrid Attention Architecture: GLM-5.3-Flash adopts a hybrid architecture of linear attention and sparse attention. Linear attention captures local dependencies through a recursive mechanism, while sparse attention recalls global context with the help of a lightweight indexer; at the same time, IndexPool is introduced to compress the indexer cache vectors from 4 to 1, reducing attention computation and KV cache by 3.01 times and 4.44 times respectively compared with GLM-5.3, greatly lowering service costs while maintaining precise long-context capabilities.
Manifold-Constrained Hyper-Connections (mHC): The model adopts mHC technology to improve Scaling capability, enabling it, while keeping total parameter count at 320B comparable to GLM-4.5, to reduce activated parameters from 32B to 18B and layers from 92 to 45. Combined with 30T Token multimodal pre-training corpus, it achieves performance surpassing GLM-5.2 with fewer computational resources.
Domestic Chip Inference Engine: Targeting the memory capacity and bandwidth bottlenecks of domestic chips, the team built a dedicated inference engine on the basis of SGLang, adopting an EPD (Encode-Prefill-Decode) disaggregated architecture to split multimodal encoding, prefill, and decode into independent worker pools. Combined with technologies such as tensor parallelism, ReplaySSM, W8A8 quantization, and hybrid cache quantization, it improves end-to-end performance by 3 times over the baseline, reaching efficiency comparable to mainstream NVIDIA GPUs.

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How to Use GLM-5.3-Flash

BigModel API: Visit the BigModel open platform https://docs.bigmodel.cn/cn/guide/models/vlm/glm-5.3-flash注册账号，获取 API Key, then call the glm-5.3-flash interface according to the documentation.
Z.ai API: Visit the Z.ai API official website https://docs.z.ai/guides/vlm/glm-5.3-flash文档中心，注册并获取 API Key to access the model.
Z.ai Online Chat: Visit Z.ai the webpage, log in and start multimodal conversations directly.
Zhipu Qingyan App: DownloadZhipu Qingyan App and log in, then select GLM-5.3-Flash from the model list to use it.
ZCode: Enter ZCode, enable the model, then let the Agent collaborate on development tasks in the code editor.
AutoClaw: Visit AutoClaw official website, upload documents to let the model automatically handle office and professional document tasks.
Open-source local deployment: Download the model weights from HuggingFace, and load and run them on a local server according to the instructions.
GLM Coding Plan: Go to https://bigmodel.cn/glm-coding to apply for a limited experience card and try the model's capabilities for free.

Core advantages of GLM-5.3-Flash

Ultimate cost-effectiveness: Intelligence on par with Claude Opus 4.8, priced at only 1/40 of it, and during the limited-time discount at 1/20 of GLM-5.3.
Native multimodality: The first native multimodal model in the GLM-5 series, with vision capabilities natively integrated, able to autonomously judge and call upon visual feedback.
Efficient architecture: Adopts a hybrid architecture of sparse attention and linear attention, reducing attention computation and KV cache by 3.01x and 4.44x respectively compared with GLM-5.3.
Domestic chip deployment: For the first time, a domestic chip cluster is used at large scale, improving end-to-end performance by 3x over the baseline, with efficiency comparable to mainstream NVIDIA GPUs.
Comprehensively surpasses the previous generation: With 320B total parameters / 18B activated parameters, it comprehensively surpasses GLM-5.2, which has twice the parameter count, in multiple benchmark tests.
Optimized for professional work: Deeply optimized for professional scenarios such as finance, law, and Office documents, with output formats ready for direct delivery.

Project address of GLM-5.3-Flash

Project official website：https://z.ai/blog/glm-5.3-flash
HuggingFace model repository：https://huggingface.co/zai-org/GLM-5.3-Flash

Comparison of GLM-5.3-Flash with similar competing products

Dimension	GLM-5.3-Flash	Claude Opus 4.8	GLM-5.3
Total parameters	320B	Not disclosed	About 355B
Activated parameters	18B	Not disclosed	32B
Number of model layers	45 layers	Not disclosed	92 layers
AA Intelligence Index	57 points	57 points	Higher (about 60+)
Multimodal capabilities	✅ Native visual encoding	✅ Multimodal	✅ Multimodal
Open-source status	✅ Open source	❌ Closed source	❌ Closed source
Pricing level	Extremely low (1/40 of Opus 4.8)	High-end pricing	Mid-range pricing (10~20x that of Flash)
Attention architecture	Sparse + linear hybrid	Standard Transformer	Standard Transformer
Long context support	1M	200K+	1M
Domestic Chip Deployment	✅ Verified	❌ No	❌ No
Core Positioning	Frontier Intelligence for All	Top Closed-Source Models	Previous-Generation Flagship Models

Application Scenarios of GLM-5.3-Flash

Front-End and Game Development: Autonomously iterates code based on visual feedback, generating runnable web pages, games, and 3D scenes.
Professional Financial Work: Covers the entire workflow of financial research, report generation, modeling, and analysis, providing traceable data sources.
Legal Practice Handling: Reviews contract clauses, annotates and revises, and drafts lawyer's letters and litigation documents that conform to practice standards.
Office Document Automation: Handles formats such as PPTX, PDF, DOCX, and XLSX, with aesthetic judgment and layout optimization capabilities.
Agent Collaborative Office Work: Automatically collaborates across browsers, code, and GUIs through Browser Use Agent and Computer Use Agent.
Everyday Multimodal Conversation: Performs image-text understanding, creative generation, and knowledge Q&A in Z.ai and the Zhipu Qingyan App.