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GLM 4.7: A Comprehensive Review & Performance Analysis

Key Concepts:

• GLM 4.7: A newly released, open-source large language model (LLM) developed by ZAI, currently considered the leading open-source model, rivaling closed-source models like Gemini 3 Pro and GPT-5.2.
• Agentic Coding: The ability of an AI model to autonomously develop and refine code based on prompts, going beyond simple code generation.
• Interleaved Thinking: A process where the AI model explicitly thinks before responding, improving instruction following and reducing hallucinations.
• Preserved Thinking: A mechanism enabling the AI to retain previous thoughts and context over extended interactions, enhancing long-term memory.
• Turnule Thinking: A feature allowing the AI to bypass deep thinking for simpler tasks, optimizing computational resources.
• Context Window: The amount of text (measured in tokens) an AI model can process at once; GLM 4.7 has a 200,000 token context window.
• Open-Source vs. Closed-Source Models: Open-source models are publicly available for download and modification, offering data privacy advantages, while closed-source models are proprietary and accessed via APIs.
• Parameters: The adjustable variables within an AI model that are learned during training; GLM 4.7 has 358 billion parameters.

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I. Introduction & Initial Performance Claims

The video focuses on GLM 4.7, a new open-source LLM released by ZAI, positioned as surpassing previous open-source leaders like Deepseek and Kimik K2, and even competing with top closed-source models such as Gemini 3 Pro and GPT-5.2. The presenter emphasizes the model’s capabilities in complex tasks like agentic coding and deep research, rather than basic functions like email drafting. The model is accessible for free via an online chat platform (link in description).

II. Demonstrations & Capabilities

A series of demonstrations showcase GLM 4.7’s abilities:

• Android OS Simulation: The model successfully generated HTML code for a functional Android OS simulation with a lock screen, home screen with app icons (Chrome, Play Store, Photos, Camera), navigation bar, and a pull-down notification shade (Wi-Fi, Bluetooth, flashlight). The process involved iterative prompting to add functionality like working app pages (Messages, Maps, Settings, Music), with the map app integrating OpenStreetMap.
• Fruit Ninja Game: GLM 4.7 created a playable Fruit Ninja game using webcam hand tracking in just two prompts. The game included features like fruit slicing, a scoring system (hearts), and a bomb mechanic.
• Isometric 3D City Builder: The model developed a Sim City-like game with building construction (houses, offices, factories, hospitals, malls, roads, parks), a tax system impacting happiness, and the need to maintain a happiness level above 95%. Further prompting refined the game logic, ensuring buildings connected to roads functioned correctly and that different building types affected happiness accordingly.
• Online Video Editor: GLM 4.7 generated a functional online video editor with a timeline, media library, drag-and-drop functionality, clip duration adjustment, and video effects (opacity, blur, brightness, contrast, saturation, grayscale, sepia, hue rotate, invert). Initial errors were resolved through iterative prompting.
• 3D Racing Game: The model created a 3D racing game with speed boosts and collision physics, utilizing publicly available assets. Refinement prompts improved visuals and ensured game functionality.
• Trello Clone: GLM 4.7 built a Trello-like task management application with Kanban boards, drag-and-drop cards, color tagging, priority setting, and multiple view options (Kanban, Gantt chart, calendar).
• Drag-and-Drop UI Builder: The model generated a UI builder similar to Figma, with snap-to-grid, alignment guides, HTML export, a component library, and responsive breakpoints.
• Taxonomy Tree Visualization: Given an image of a complex taxonomy tree, GLM 4.7 created an interactive node graph in HTML, allowing users to explore the data.
• Financial Report Generation: From a complex financial spreadsheet, the model generated a comprehensive financial report with interactive graphs and charts.
• Medical Research Report: Based on a detailed prompt regarding a rare enzymatic deficiency, GLM 4.7 produced a thorough research report with an executive summary, pathophysiology details, comparisons of disease onset types, and an overview of gene therapy efficacy, including cited references.

III. Technical Specifications & Performance Benchmarks

• Parameters: 358 billion
• Context Window: 200,000 tokens (approximately 150,000 words)
• Benchmarks: GLM 4.7 demonstrated competitive performance on various benchmarks, including:
  • AIM (Competitive Math): Outperformed Cloud 4.5 and GPT-5.1.
  • GPQ8 Diamond (Graduate Level Science): Showed strong results.
  • Humanity’s Last Exam: Achieved 42.8%, surpassing Gemini 3 Pro (38.3%) and GPT-5.2 (29.9%).
  • SweetBench Verified: Scored 73.8, exceeding GPT-5.2 (71.8).
• Comparison to Gemini 3 Pro: While Gemini 3 Pro generally performs better overall, GLM 4.7 excels in specific areas like competitive math.
• Open-Source Availability: The model is available for download on Hugging Face, requiring significant computational resources (717 GB total size).

IV. Core Technological Advancements

GLM 4.7 incorporates three key thinking mechanisms:

• Interleaved Thinking: Enhances instruction following and reduces hallucinations by prompting the model to think before responding.
• Preserved Thinking: Improves long-term memory and consistency by retaining previous thoughts during extended interactions.
• Turnule Thinking: Optimizes computational efficiency by disabling deep thinking for simpler tasks.

V. Advantages of Open-Source Models & ZAI’s Contribution

The presenter highlights the benefits of open-source models, particularly data privacy, as they allow companies to process sensitive information without relying on third-party servers. He commends ZAI for releasing such a powerful model (GLM 4.7) publicly, noting the significant investment required for its development.

VI. Conclusion & Key Takeaways

GLM 4.7 represents a significant advancement in open-source LLMs, demonstrating capabilities comparable to leading closed-source models. Its strengths lie in agentic coding, deep research, and a remarkably low error rate. The model’s open-source nature provides valuable benefits for data privacy and customization. The presenter encourages viewers to explore GLM 4.7 and share their experiences. He also promotes his weekly AI newsletter for staying updated on the rapidly evolving AI landscape.

Notable Quote:

“GLM just works. This has got to be the model with the least amount of errors in its generations.” – Presenter, emphasizing the model’s reliability.