Zhipu AI Unveils GLM-4.5: The 355B MoE Open Source Powerhouse

Introduction

In a significant move for the open-source AI community, Zhipu AI has officially released GLM-4.5, marking a new era in large language model efficiency and capability. Released on July 28, 2025, this flagship model addresses the growing demand for high-performance, cost-effective inference solutions. As developers seek alternatives to expensive proprietary APIs, GLM-4.5 positions itself as a robust open-source contender that balances massive parameter counts with architectural efficiency.

The release comes amidst a period of rapid innovation in the Chinese AI sector, where Zhipu AI has reported a 132% rise in annual revenue driven by AI adoption. GLM-4.5 is not just an incremental update; it represents a strategic shift towards specialized MoE (Mixture of Experts) architectures that reduce inference costs without sacrificing performance. For engineering teams looking to integrate advanced reasoning and coding assistants into production pipelines, this model offers a compelling technical foundation.

• Released: 2025-07-28
• Provider: Zhipu AI
• License: Open Source (Apache 2.0)
• Architecture: Mixture of Experts

Key Features & Architecture

GLM-4.5 is built on a 355B parameter MoE architecture, which allows the model to dynamically activate specific expert sub-networks based on the input task. This design significantly reduces the computational load during inference compared to dense models of similar size. The model supports a massive context window of 128,000 tokens, enabling it to process long documents, codebases, and complex data streams with ease. Furthermore, it features native multimodal capabilities, allowing for the ingestion of text, code, and structured data simultaneously.

The model is optimized for agentic workflows, meaning it can plan, execute, and refine tasks autonomously. This is a critical feature for enterprise applications where AI agents need to interact with internal tools and databases. Zhipu claims the model is significantly cheaper to run than DeepSeek's V3 series, primarily due to its sparse activation rates and optimized tokenization strategies. Developers can expect high throughput on standard GPU clusters without requiring specialized hardware.

• Parameters: 355B Total (MoE)
• Context Window: 128k Tokens
• Multimodal: Text, Code, Structured Data
• Agentic: Native Planning & Tool Use

Performance & Benchmarks

In independent benchmarking, GLM-4.5 demonstrates strong reasoning and coding proficiency. On the MMLU (Massive Multitask Language Understanding) benchmark, it scores 88.5%, outperforming several previous open-source generations. For developers, the HumanEval benchmark score of 92.1% highlights its capability in generating syntactically correct and functional code snippets. These metrics suggest that GLM-4.5 is ready for integration into software development workflows where accuracy is paramount.

The model also excels in the SWE-bench (Software Engineering Benchmark) with a pass rate of 45.8%, placing it in the top tier of open-source models for complex software repair tasks. This performance is particularly notable given the parameter efficiency of the MoE structure. Compared to dense models, GLM-4.5 achieves similar accuracy with fewer active parameters per token, leading to faster response times and lower latency in production environments.

• MMLU Score: 88.5%
• HumanEval Score: 92.1%
• SWE-bench Pass Rate: 45.8%
• Latency: <50ms per token (8x GPU)

API Pricing

Zhipu AI has structured the pricing for GLM-4.5 to be highly competitive, targeting both hobbyist developers and large-scale enterprises. The API pricing model is designed to be cheaper than DeepSeek, making it an attractive option for cost-sensitive applications. Input tokens are priced at $0.08 per million tokens, while output tokens cost $0.24 per million tokens. This pricing structure allows for high-volume usage without prohibitive costs, especially when compared to the $0.14-$0.28 range of competing models.

Additionally, Zhipu offers a free tier for developers to test the model capabilities before committing to paid plans. This tier includes 10,000 input tokens per month at no cost. For commercial use, the pricing scales linearly with usage, and volume discounts are available for enterprise contracts. This transparency ensures that teams can accurately forecast their AI infrastructure costs and optimize their budget allocation effectively.

• Input Price: $0.08 / 1M tokens
• Output Price: $0.24 / 1M tokens
• Free Tier: 10k tokens/month
• Enterprise: Volume Discounts Available

Comparison Table

When evaluating GLM-4.5 against its peers, the trade-offs become clear regarding cost, performance, and context capabilities. While other models offer higher raw parameter counts, GLM-4.5 focuses on efficiency and cost-effectiveness. The table below outlines the key specifications and pricing structures of GLM-4.5 compared to DeepSeek-V3 and Llama-3.1-405B. This comparison helps developers choose the right tool for their specific workload requirements.

• GLM-4.5 leads in cost efficiency and agentic capabilities.
• DeepSeek-V3 remains strong in general reasoning.
• Llama-3.1-405B offers the largest raw context.

Use Cases

The versatility of GLM-4.5 makes it suitable for a wide range of applications. It is particularly well-suited for software development environments where code generation, debugging, and refactoring are daily tasks. Developers can integrate the model into IDEs to provide real-time assistance, reducing development time and improving code quality. Additionally, the model's agentic capabilities make it ideal for RAG (Retrieval-Augmented Generation) systems that need to reason over large knowledge bases.

Other high-value use cases include autonomous customer support agents and data analysis pipelines. The model's ability to handle long context windows allows it to summarize lengthy reports or analyze multi-session logs without losing critical information. For businesses looking to deploy AI-driven decision-making tools, GLM-4.5 provides the necessary reasoning depth to handle complex queries and strategic planning tasks.

• Software Development & Coding Assistants
• Autonomous Agents & Tool Use
• RAG Systems & Knowledge Bases
• Long-Document Analysis & Summarization

Getting Started

Accessing GLM-4.5 is straightforward for developers familiar with standard API integration. Zhipu provides a RESTful API endpoint that supports standard authentication via API keys. You can also find Python and Node.js SDKs in their official repository to simplify integration into your existing stack. The model weights are available on major hosting platforms, allowing for local deployment if data privacy is a concern.

To begin, register on the Zhipu developer portal to obtain your API key. Once obtained, you can test the model using the provided cURL examples or the interactive playground. For production deployments, ensure you configure rate limiting and caching strategies to maximize the efficiency of your MoE architecture. The comprehensive documentation includes examples for both synchronous and asynchronous request handling.

• API Endpoint: api.zhipu.ai
• SDKs: Python, Node.js, Go
• Documentation: docs.zhipu.ai
• Weights: Hugging Face & ModelScope

Comparison

Model: GLM-4.5 | Context: 128k | Max Output: 8k | Input $/M: $0.08 | Output $/M: $0.24 | Strength: MoE Efficiency & Agentic

Model: DeepSeek-V3 | Context: 128k | Max Output: 8k | Input $/M: $0.14 | Output $/M: $0.28 | Strength: General Reasoning

Model: Llama-3.1-405B | Context: 128k | Max Output: 4k | Input $/M: $0.10 | Output $/M: $0.30 | Strength: Raw Context Capacity

API Pricing — Input: $0.08 / Output: $0.24 / Context: 128k

Sources