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GLM5: A Comprehensive Review of the Leading Open-Source Model

Key Concepts:

GLM5: The latest open-source large language model (LLM) by Z AI, currently considered the best available.
Agent Capability: The ability of the model to autonomously perform complex, multi-step tasks, including web search and tool usage.
Mixture of Experts (MoE): An architecture where the model utilizes a subset of its parameters for each task, increasing efficiency.
Context Window: The amount of text the model can process at once (200,000 tokens for GLM5).
Hallucination Rate: The frequency with which the model generates factually incorrect or nonsensical information.
Reinforcement Learning (RL): A training method that uses feedback to improve model performance.
Sparse Attention: A mechanism to handle long prompts efficiently.
Open Source: Software with publicly accessible source code, allowing for local deployment and customization.

I. Introduction & Overview

The video introduces GLM5 by Z AI as a new leading open-source LLM, rivaling the performance of top closed-source models like Gemini and ChatGPT. It’s available for free trial at Z.AI and boasts impressive capabilities, particularly its “agent” functionality. The video aims to demonstrate GLM5’s features, specifications, and performance benchmarks. The video is sponsored by HubSpot, who also provide a related "AI Agents Unleashed Playbook for 2026 Success."

II. Agent Capabilities & Demonstration: Chemistry Course Creation

A core feature of GLM5 is its agent capability, allowing it to autonomously tackle complex tasks. The video demonstrates this by prompting the agent to create a comprehensive educational chemistry course for kids, including lessons, images, and interactive exercises.

The agent operates through a defined process:

To-Do List Creation: Planning the course structure with kid-friendly lessons.
Image Generation: Creating engaging visuals using ZI’s own GLM Image model.
Lesson & Exercise Development: Building out the course content with interactive elements.
Testing & Finalization: Ensuring the application functions correctly.

The agent builds the course within a “sandbox environment,” creating multiple pages accessible sequentially. Unlike a standard chat interaction (limited to one page at a time), the agent delivers a complete, multi-page course. The generated lesson one includes images and a functional quiz with interactive animations and popups. Subsequent lessons cover atoms (with interactive building exercises) and elements (with a matching game). A temporary issue with a Chinese language display and a misconfigured periodic table in the elements lesson was noted.

III. Agent Capabilities & Demonstration: OS Development

The video further showcases the agent’s capabilities by tasking it with designing a mobile operating system (OS) superior to Android and iOS. The prompt requests eight apps on the home screen, along with explanations of design choices and rationale.

The agent’s process mirrors the chemistry course creation:

Design & Planning: Conceptualizing the OS and its apps.
Page Creation: Building the interface.
Interactive App Screens & Animations: Implementing the app functionality.
Rationale Documentation: Explaining the design decisions.

The resulting OS concept includes apps like:

Pulse: A unified health dashboard combining Apple Health and Fitbit data.
Canvas: A drawing application.
Nexus: An AI assistant similar to Siri or Google Assistant, with broader app integration.
Flow: A unified communication hub for SMS, email, social media, etc.
Vault: A privacy and security focused app.
Horizon: A smart context engine displaying weather, events, and suggestions.
Mirror: A digital well-being app with focus mode and usage tracking.
Bridge: A seamless device connection tool (similar to AirDrop).

IV. Chat Mode & Coding Demonstration: Physics Simulation

The video contrasts the agent mode with the standard chat mode, highlighting the latter’s speed for simpler tasks. A prompt to create a real-time simulation of two metallic spheres above a street scene in HTML demonstrates this. Initial results lacked a realistic background, which was corrected with a subsequent prompt. The model initially failed to render reflections between the spheres, requiring further prompting to achieve the desired effect. The final simulation allows users to adjust sphere properties like reflectivity and roughness.

V. Multimodal Capabilities & Data Analysis

GLM5’s multimodal capabilities are demonstrated by uploading an image and prompting the model to create a 3D animated scene. The initial result displayed a nighttime setting despite no such request. Subsequent prompting allowed control over the time of day and lighting. The model also successfully created a piano roll interface and composed a piano piece (though the quality of the composition was questioned).

The video then demonstrates GLM5’s ability to analyze financial data. Uploading Q4 earnings reports from Google, Nvidia, and Amazon prompted the model to generate a consolidated spreadsheet with financials, charts, growth forecasts, and recommendations. The model accurately analyzed the data and provided insightful recommendations, even correctly predicting Google’s subsequent stock performance. A similar demonstration involved creating a stock chart visualizer from Yahoo Finance data, allowing users to adjust visualizations and replay historical data.

VI. Technical Specifications & Performance Benchmarks

GLM5 boasts 744 billion parameters, more than double the 355 billion of GLM 4.5. It utilizes a Mixture of Experts (MoE) architecture, activating only 40 billion parameters at a time for efficiency. The model incorporates Deepseek’s sparse attention mechanism for handling long prompts. GLM5 has a 200,000-token context window (approximately 150,000 words).

Performance benchmarks demonstrate GLM5’s competitiveness with top closed-source models:

Humanity’s Last Exam: GLM5 outperforms leading closed models.
SWEBench, Verified, Multilingual, Terminal Bench: GLM5 performs on par with top closed models in coding tasks.
BrowseComp: GLM5 significantly outperforms other models in web browsing tasks.
Vending Bench 2: GLM5 rivals GPT-4 and Gemini 3 Pro in simulated business operations.
Artificial Analysis Leaderboard: GLM5 currently ranks as the top open-source model, nearing the performance of GPT-5.2 Extra High and Claude Opus 4.6.

VII. Cost & Accessibility

GLM5 is available for free trial on Z.AI. The open-source model is available on Hugging Face, though requires significant computational resources (1.5TB). API access is available through a subscription model, starting at $27 per quarter ($9/month), significantly cheaper than competitors. GLM5 also boasts a lower hallucination rate compared to other models, as measured by the Artificial Analysis Omniscience benchmark.

VIII. HubSpot’s AI Agents Playbook

The video promotes HubSpot’s “AI Agents Unleashed Playbook for 2026 Success,” which provides a comprehensive guide to understanding and implementing AI agents. The playbook covers the differences between chatbots and agents, the current state of AI agents, real-world use cases, and practical guidance for getting started. It emphasizes the importance of human oversight and the evolving role of humans as “agent orchestrators.”

IX. Conclusion

GLM5 represents a significant advancement in open-source LLMs, offering performance comparable to leading closed-source models at a fraction of the cost. Its agent capabilities, multimodal functionality, and low hallucination rate make it a powerful tool for a wide range of applications, from education and creative content generation to coding and data analysis. The availability of the open-source model and affordable API access democratize access to advanced AI technology.