GLM-4: Zhipu AI's Open-Source Bilingual LLM

The landscape of large language models has been dominated by English-first development. OpenAI, Anthropic, Google, Meta, and Mistral all built their flagship models with English as the primary language, adding multilingual capabilities as an afterthought through translation or mixed training data. This creates real problems for the billions of users who primarily interact with AI in non-English languages – Chinese in particular, which represents the world’s largest language community.

GLM-4, developed by Zhipu AI (智谱AI) – one of China’s leading AI companies, backed by Tsinghua University researchers – takes a fundamentally different approach. It is a bilingual foundation model built from the ground up for both Chinese and English, with neither language treated as secondary. The result is a model that matches or exceeds GPT-4 on Chinese benchmarks while remaining competitive on English tasks, positioning it as the leading open-source Chinese-English bilingual LLM in 2026.

The GLM architecture itself deserves attention. Unlike GPT-style decoder-only models, GLM (General Language Model) uses a unified pretraining framework that combines autoregressive blank infilling with multi-task learning. This architecture was originally proposed in a 2024 paper by Zhipu AI and Tsinghua University, and it has proven particularly effective for Chinese text understanding, where the model’s bidirectional attention helps capture the contextual nuances of Chinese characters and their compound meanings.

Performance Benchmarks

GLM-4 demonstrates strong performance across both Chinese and English benchmarks:

The data reveals GLM-4’s particular strengths: it leads on Chinese benchmarks (C-Eval, CMMLU) and mathematical reasoning (GSM8K), while remaining competitive on English tasks and coding. This makes it an excellent choice for bilingual applications serving Chinese and English users simultaneously.

Model Architecture and Variants

The GLM-4 model ecosystem includes several variants optimized for different deployment scenarios:

flowchart TD
    subgraph Base[Base Models]
        GLM4-9B[GLM-4-9B<br>9.4B params<br>Consumer GPUs]
        GLM4-130B[GLM-4-130B<br>130B params<br>Enterprise GPUs]
    end

    subgraph Quantized[Quantized Variants]
        GLM4-9B-Int4[GLM-4-9B-Int4<br>~5GB VRAM]
        GLM4-130B-Int8[GLM-4-130B-Int8<br>~65GB VRAM]
    end

    subgraph FineTuned[Fine-Tuned Variants]
        GLM4-Chat[GLM-4-Chat<br>Dialogue Optimized]
        GLM4-Code[GLM-4-Code<br>Code Specialized]
        GLM4-Tool[GLM-4-Tool<br>Tool Use Optimized]
    end

    GLM4-9B --> GLM4-9B-Int4
    GLM4-130B --> GLM4-130B-Int8
    GLM4-9B --> GLM4-Chat
    GLM4-130B --> GLM4-Code
    GLM4-130B --> GLM4-Tool

Deployment and Accessibility

GLM-4 is available through multiple channels, making it accessible to both researchers and commercial users:

The GLM-4 GitHub repository provides model cards, inference code, fine-tuning scripts, and deployment guides.

FAQ

What is GLM-4?

GLM-4 is Zhipu AI’s open-source bilingual (Chinese-English) large language model, built on the General Language Model (GLM) architecture. It delivers strong performance on reasoning, coding, mathematics, and multilingual tasks, with particular strength in Chinese language understanding.

How does GLM-4 compare to GPT-4 on Chinese tasks?

GLM-4 achieves competitive or superior performance on Chinese-language benchmarks including C-Eval, CMMLU, and Chinese commonsense reasoning tasks. It particularly excels at Chinese-specific applications like classical Chinese translation, Chinese legal document analysis, and Chinese literature understanding.

Is GLM-4 open source?

Yes, Zhipu AI has open-sourced GLM-4 under permissive licensing through the ModelScope and Hugging Face platforms. The model weights are available for research and commercial use, though some larger variants may require approval for extremely high-volume commercial deployments.

What is the GLM architecture?

GLM (General Language Model) is an autoregressive architecture with bidirectional attention, originally proposed by Zhipu AI and Tsinghua University researchers. It combines the strengths of encoder-only models (like BERT) for understanding tasks and decoder-only models (like GPT) for generation tasks.

What model sizes are available?

GLM-4 is available in multiple sizes: GLM-4-9B for lightweight deployment, GLM-4-130B for full capability, and quantized variants (Int4, Int8) for efficient inference on consumer GPUs. The 9B variant can run on a single RTX 4090 with quantization.

Further Reading

• GLM-4 GitHub Repository – Source code, model weights, and documentation
• Zhipu AI Official Site – API access, enterprise offerings, and research publications
• GLM-4 on Hugging Face – Model weights and inference examples
• GLM-130B Research Paper – The original GLM architecture paper from Tsinghua University