GLM-4.5: Zhipu AI's Next-Gen Multimodal Foundation Model

The evolution of foundation models in 2025-2026 has been defined by two trends: multimodality and efficiency. Models that could only process text have rapidly given way to models that natively understand images, audio, and video. Meanwhile, Mixture-of-Experts (MoE) architectures have become the standard approach for building models that are both powerful and practical to deploy. Zhipu AI’s GLM-4.5 represents the convergence of these trends in the Chinese AI ecosystem.

GLM-4.5 is Zhipu AI’s next-generation foundation model, building on the GLM-4 architecture with native multimodal understanding, significantly improved reasoning capabilities, and an efficient MoE design. The model represents China’s most ambitious open-source AI release to date, competing directly with GPT-4o, Claude 4 Sonnet, and Gemini 2.5 across both Chinese and English benchmarks.

The jump from GLM-4 to GLM-4.5 is substantial. While GLM-4 was primarily a text model with some vision capabilities added post-hoc, GLM-4.5 is natively multimodal: it processes images, audio, and video as first-class inputs alongside text. The reasoning pipeline has been overhauled with chain-of-thought capabilities and structured tool use that rivals the best Western models. And the MoE architecture delivers GPT-4-class capabilities at a fraction of the inference cost.

Architectural Improvements

The architectural differences between GLM-4 and GLM-4.5 are significant:

Multimodal Processing Pipeline

GLM-4.5 processes multiple input modalities through a unified architecture:

flowchart LR
    subgraph Inputs[Input Modalities]
        Text[Text Input]
        Image[Image Input]
        Audio[Audio Input]
        Video[Video Input]
    end

    subgraph Encoders[Modality Encoders]
        TE[Text Encoder<br>GLM Tokenizer]
        IE[Vision Encoder<br>SigLIP ViT]
        AE[Audio Encoder<br>Whisper-style]
        VE[Video Encoder<br>Spatio-temporal]
    end

    subgraph Projection[Cross-Modal Projection]
        Proj[Learned Projection Layer]
    end

    subgraph MoE[MoE Transformer Backbone]
        MoELayer1[MoE Layer 1<br>8 experts, top-2 routing]
        MoELayer2[MoE Layer 2<br>8 experts, top-2 routing]
        MoELayerN[MoE Layer N<br>8 experts, top-2 routing]
    end

    subgraph Outputs[Generation]
        Decoder[Output Decoder]
        TextOut[Generated Text]
    end

    Text --> TE
    Image --> IE
    Audio --> AE
    Video --> VE

    TE --> Proj
    IE --> Proj
    AE --> Proj
    VE --> Proj

    Proj --> MoELayer1
    MoELayer1 --> MoELayer2
    MoELayer2 --> MoELayerN
    MoELayerN --> Decoder
    Decoder --> TextOut

The architecture performs modality-specific encoding, projects all modalities into a shared latent space, processes them through the MoE transformer backbone, and generates text output. This unified approach means GLM-4.5 can reason across modalities in a single forward pass: describing the contents of an image while referring to accompanying text, or transcribing audio while analyzing its relationship to a video frame.

Performance Benchmarks

GLM-4.5 achieves competitive scores against leading models across multiple benchmark categories:

GLM-4.5 leads on Chinese knowledge benchmarks and math reasoning, holds its own on multimodal tasks, and shows strong agentic performance. It trails Claude 4 Sonnet on coding but remains competitive with GPT-4o and Gemini 2.5 Pro.

Enterprise Applications

The model’s multilingual and multimodal capabilities make it particularly suitable for:

• Chinese enterprise knowledge management with document analysis
• Cross-lingual customer service combining text, images, and audio
• Video content analysis and summarization for Chinese media
• Educational applications requiring both Chinese and English support
• Healthcare image analysis with Chinese medical terminology

Getting Started

Visit the GLM-4.5 GitHub repository for model cards, inference examples, and documentation. The smaller variants are available on Hugging Face for local deployment, while the full model can be accessed through Zhipu AI’s API.

FAQ

What is GLM-4.5?

GLM-4.5 is Zhipu AI’s next-generation multimodal foundation model that natively processes text, images, audio, and video inputs with enhanced reasoning capabilities, improved agentic performance, and stronger Chinese-English bilingual understanding than its predecessor GLM-4.

What new capabilities does GLM-4.5 add over GLM-4?

GLM-4.5 adds native multimodal input (images, audio, video), improved reasoning through chain-of-thought and function calling, extended context windows of up to 128K tokens, enhanced tool use, and a new Mixture-of-Experts architecture that improves efficiency.

How does GLM-4.5 compare to GPT-4o and Claude 4?

GLM-4.5 is competitive with GPT-4o on vision-language tasks and outperforms it on Chinese multimodal understanding. On pure text reasoning, Claude 4 still leads, but GLM-4.5 closes the gap significantly while offering better bilingual performance and a more efficient MoE architecture.

What is the MoE architecture in GLM-4.5?

GLM-4.5 uses a Mixture-of-Experts (MoE) architecture with approximately 400 billion total parameters, activated at about 45 billion per token. This means it has the capacity of a 400B model with the inference cost of a 45B model, making it dramatically more efficient than the dense 130B-parameter GLM-4.

Is GLM-4.5 open source?

Zhipu AI has open-sourced the smaller variants of GLM-4.5 (up to 9B parameters) under a permissive license. The full 400B MoE variant is available through Zhipu’s API and through the ModelScope platform for approved research partners.

Further Reading

• GLM-4.5 GitHub Repository – Source code, model cards, and deployment guides
• Zhipu AI Official Site – API access and enterprise solutions
• GLM-4 Complete Guide – Deep dive into the GLM-4 predecessor model
• ModelScope Platform – Chinese AI model hosting and distribution platform