China’s Z.ai (formerly Zhipu AI) has just released GLM-4.5, an impressive open-source language model that’s making waves in the AI community. After testing it myself with a nearly 2000-line codebase, I can confirm that this model delivers on its promise of handling large contexts with remarkable performance. Let’s dive into what makes GLM-4.5 a compelling choice for developers and researchers.
What is GLM-4.5?
GLM-4.5 is China’s Z.ai latest open-source language model featuring an “agent-native” architecture that integrates reasoning, coding, and tool-using capabilities with impressive speed and efficiency (Z.ai, 2025). Available in two variants: GLM-4.5 (355B total/32B active parameters) and GLM-4.5-Air (106B total/12B active parameters). Both utilize efficient Mixture of Experts (MoE) architecture for optimal performance and fast inference.
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Core Features and Real-World Testing
128K Context Window with Strong Multi-File Understanding
GLM-4.5 provides a 128,000-token context window (Z.ai, 2025). While not the largest available (Gemini offers over 1 million tokens), it demonstrates exceptional practical utility in maintaining coherent understanding across complex, multi-file scenarios.
In my testing, I put GLM-4.5 through two coding scenarios:
Test 1: Multi-File Code Analysis – Uploading and analyzing a codebase consisting of nearly 2000 lines worked seamlessly, with the model maintaining coherent understanding throughout the entire context and successfully handling cross-file references and dependencies.
Test 2: Multi-File Chart Integration – I presented GLM-4.5 with three HTML files containing charts and asked it to take one chart from the first file, another chart from the second file, and merge them into the third file.
GLM-4.5 successfully completed this complex task, demonstrating sophisticated understanding of HTML structure, chart libraries, data integration, and cross-file referencing, a task where both Microsoft Copilot and Google Gemini failed completely.
Hybrid Reasoning Architecture
GLM-4.5 offers dual processing modes (Z.ai, 2025):
- Thinking Mode: Step-by-step analysis for complex reasoning
- Non-Thinking Mode: Instant responses for straightforward queries
Native Function Calling
Built-in function calling capabilities make GLM-4.5 exceptionally well-suited for agentic applications without requiring external frameworks (Z.ai, 2025).
Performance Benchmarks and Competitive Position
GLM-4.5 achieves 3rd place overall in comprehensive benchmarking across 12 key metrics, trailing only OpenAI’s o3 and Grok 4, while outperforming Claude 4 Opus and Claude 4 Sonnet (Z.ai, 2025).
Where GLM-4.5 Leads
Agentic Tasks: GLM-4.5 dominates in tool-using applications:
- Matches Claude 4 Sonnet on τ-bench and BFCL-v3 benchmarks (Z.ai, 2025)
- Outperforms Claude 4 Opus on web browsing tasks (26.4% vs 18.8% on BrowseComp) (Z.ai, 2025)
- Best-in-class tool calling: 90.6% success rate, beating Claude 4 Sonnet (89.5%) and all other tested models (Z.ai, 2025)
Where It’s Competitive
Reasoning Capabilities: Strong middle-tier performance (Z.ai, 2025):
- 84.6% on MMLU Pro (vs Claude 4 Opus 87.3%, Gemini 2.5 Pro 86.2%)
- 91.0% on AIME24 mathematical reasoning (vs leading Grok 4’s 94.3%)
- 98.2% on MATH 500 (matching Claude 4 Opus performance)
Coding Performance: Solid but not leading (Z.ai, 2025):
- 64.2% on SWE-bench Verified (vs Claude 4 Sonnet 70.4%)
- 37.5% on Terminal-Bench (vs Claude 4 Opus 43.2%)
- Excels in full-stack development and complex artifact generation
The Open-Source Advantage
Unlike its proprietary competitors (GPT-4, Claude, Gemini), GLM-4.5 offers open weights and local deployment options (Z.ai, 2025), making it significantly more cost-effective and customizable while delivering performance that approaches frontier models. While OpenAI, Anthropic, and Google maintain their most capable models behind expensive API paywalls, GLM-4.5 democratizes access to frontier-level AI capabilities. The implications are profound:
Economic Impact: Organizations can deploy GLM-4.5 without ongoing API costs, making advanced AI accessible to startups, researchers, and developing nations that couldn’t otherwise afford enterprise-grade AI capabilities.
Innovation Acceleration: Open weights enable fine-tuning for specific domains, academic research without usage restrictions, and integration into products without vendor lock-in concerns.
Transparency and Trust: Unlike black-box proprietary models, GLM-4.5’s open architecture allows for security auditing, bias analysis, and understanding of model behavior, critical for sensitive applications.
Competitive Pressure: GLM-4.5’s strong performance challenges the proprietary model paradigm, potentially forcing other companies to reconsider their closed-source strategies or risk losing market share to more accessible alternatives.
Real-World Applications
GLM-4.5 excels in several practical domains (Z.ai, 2025):
Agentic Coding: The model can seamlessly integrate with existing coding toolkits like Claude Code, Roo Code, and CodeGeex. It demonstrates strong capabilities in building complete web applications from scratch, including frontend, backend, and database components.
Web Browsing and Research: With its native web browsing capabilities, GLM-4.5 can conduct complex research tasks, gathering and synthesizing information from multiple sources.
Content Creation: The model can generate presentation materials, slides, and posters, with enhanced capabilities when combined with agentic tools for information retrieval.
Complex Code Generation: From interactive mini-games to physics simulations, GLM-4.5 can create sophisticated applications across multiple programming languages and formats.
Technical Architecture Highlights
GLM-4.5’s architecture includes several innovative design choices (Z.ai, 2025):
- Deeper, Narrower MoE Design: Unlike some competitors, GLM-4.5 increases model depth while reducing width, resulting in better reasoning capacity
- Enhanced Attention Mechanism: Uses 96 attention heads (2.5x more than typical) which consistently improves performance on reasoning benchmarks
- Advanced Training Techniques: Incorporates QK-Norm for stability and supports speculative decoding for faster inference
- Muon Optimizer: Accelerates convergence and allows for larger batch sizes during training
Limitations and Considerations
While GLM-4.5 is impressive, it’s important to understand its limitations:
Resource Requirements: Despite the MoE architecture’s efficiency, running the full GLM-4.5 model still requires significant computational resources. GLM-4.5-Air offers a more accessible alternative for resource-constrained environments.
Specialized Training Focus: The model’s RL training focuses on specific verifiable tasks (information-seeking QA and software engineering), which may limit performance in highly specialized domains outside this scope (Z.ai, 2025).
Benchmark Gaps: While competitive, GLM-4.5 doesn’t achieve state-of-the-art performance on all benchmarks. Models like OpenAI’s o3 and some other frontier models still lead in certain areas (Artificial Analysis, 2025).
Language Model Limitations: Like all current LLMs, GLM-4.5 can still hallucinate, especially when dealing with very recent information or highly specialized technical topics.
Availability and Access
GLM-4.5 is available through multiple channels (Z.ai, 2025):
- Z.ai Platform: Direct access through the web interface (https://z.ai)
- API Access: OpenAI-compatible API for integration into applications (Z.ai Documentation, 2025)
- Open Weights: Available on HuggingFace and ModelScope for local deployment (Z.ai Organization, 2025)
- Inference Frameworks: Supports vLLM and SGLang for efficient serving (Z.ai Organization, 2025)
The open-source nature of GLM-4.5 is particularly significant in today’s AI landscape, where the most capable models from OpenAI, Anthropic, and Google remain locked behind proprietary APIs. This accessibility could reshape how organizations approach AI deployment, moving from dependency on external services to sovereign AI capabilities that can be customized, audited, and deployed according to specific needs and regulatory requirements.
The Bottom Line
GLM-4.5 represents a watershed moment in AI: the first open-source model to genuinely compete with proprietary frontiers like Claude 4 Sonnet and GPT-4, ranking 3rd overall while offering what closed models cannot – complete accessibility (Z.ai, 2025).
The Open vs. Closed Paradigm Shift: While OpenAI, Anthropic, and Google lock their capabilities behind expensive APIs, GLM-4.5 delivers comparable performance with open weights. This isn’t just about cost savings. It’s about AI sovereignty: organizations can now deploy, customize, and control frontier-level AI without vendor dependency or usage restrictions.
The model’s MoE architecture provides impressive inference speed while maintaining good accuracy. Combined with its agent-native architecture, GLM-4.5 offers a strong option for teams pursuing top performance with open freedom.
References
- Z.ai. (2025). “GLM-4.5: Reasoning, Coding, and Agentic Abilities.” Z.ai Blog. https://z.ai/blog/glm-4.5
- Artificial Analysis. (2025). “GLM-4.5 – Intelligence, Performance & Price Analysis.” Artificial Analysis. https://artificialanalysis.ai/models/glm-4.5
- Z.ai Documentation. (2025). “GLM-4.5 API Documentation.” Z.ai API Docs. https://docs.z.ai/guides/llm/glm-4.5
- Z.ai Organization. (2025). “GLM-4.5 Model Collection.” HuggingFace. https://huggingface.co/collections/zai-org/glm-45-687c621d34bda8c9e4bf503b