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In the fast-evolving world of artificial intelligence, open-source models have long been chasing the performance and capabilities of their closed-source counterparts. However, a new wave of innovation coming out of China is shifting this landscape dramatically. Among the most exciting advancements is the release of GLM-4.5, a powerful open-source model developed by z AI that matchesโand in some cases even surpassesโthe best closed-source models in reasoning, coding, and agentic capabilities.
Matthew Berman, a leading AI commentator, recently showcased the groundbreaking potential of GLM-4.5 through a series of fascinating demos and benchmarks that highlight its technical prowess and practical applications. In this comprehensive article, we will dive deep into the features, demos, benchmarks, and significance of GLM-4.5, exploring why it represents a major milestone for the open-source AI community and what it means for the future of coding and reasoning AI models.
Table of Contents
- ๐ Introduction to GLM-4.5: A New Era for Open-Source AI
- ๐งฉ Impressive Demos Showcasing GLM-4.5โs Capabilities
- ๐ Benchmarking GLM-4.5: Where Does It Stand?
- ๐ง Hybrid Reasoning and Thinking Modes
- ๐ Why GLM-4.5 Matters: The Rise of Chinese Open-Source AI
- ๐ Practical Applications and Use Cases
- ๐ Bonus: Free AI Resources Recommended
- โ Frequently Asked Questions (FAQ) about GLM-4.5
- ๐ฎ Conclusion: A Bright Future for Open-Source AI
๐ Introduction to GLM-4.5: A New Era for Open-Source AI
GLM-4.5, developed by the Chinese company z AI, is a hybrid reasoning model designed to excel in both complex reasoning tasks and rapid, non-reasoning responses. It comes in two variants: the full GLM-4.5 model with 355 billion total parameters (32 billion active) and the lighter GLM-4.5 AIR model with 106 billion total parameters (12 billion active). These models utilize a mixture of experts architecture, enabling them to dynamically activate subsets of parameters depending on the task, which enhances efficiency without compromising performance.
What makes GLM-4.5 especially notable is that it achieves frontier-level performance while being fully open source. This is a significant departure from many top-performing models, which are often proprietary and closed-source, limiting accessibility and customization. With GLM-4.5, developers, researchers, and AI enthusiasts worldwide gain unprecedented access to a powerful tool capable of advanced reasoning, coding, and agentic behavior.
๐งฉ Impressive Demos Showcasing GLM-4.5โs Capabilities
Matthew Bermanโs demonstrations bring GLM-4.5โs capabilities to life, offering tangible examples of what this model can achieve. Here are some of the most striking demos:
Rubikโs Cube Simulation and Solving
One of the most captivating demos is GLM-4.5โs ability to simulate and solve Rubikโs Cubes of varying complexities. This is only the second model ever to successfully simulate a Rubikโs cube, which is a testament to its spatial reasoning and problem-solving skills.
Starting with the classic 3×3 cube, the model not only visually simulates the cube with accurate side representations but also outputs a detailed move history. This transparency allows users to verify each step of the solution. After scrambling the cube, GLM-4.5 flawlessly solves it, highlighting its understanding of the puzzle mechanics.
The model scales impressively to larger cubes as well. A 5×5 cube is scrambled and solved perfectly. Even a 10×10 cube is scrambled with 50 moves and successfully solved, despite the increased complexity. The ability to handle these larger puzzles illustrates GLM-4.5โs advanced reasoning and memory capabilities, as well as its ability to process and manage extensive sequences of operations.
Tower of Hanoi Puzzle Solver
GLM-4.5 also excels at solving the Tower of Hanoi puzzle, a classic recursive problem known for challenging AI models. Unlike some previous claims suggesting that AI models struggle with puzzles requiring chain-of-thought reasoning, GLM-4.5 demonstrates a robust ability to break down the recursive structure and solve the puzzle purely through thought.
For a 4-disc Tower of Hanoi, the model outputs all 15 moves required for the solution, accompanied by a visual simulation of the movements. The modelโs ability to generate both the solution and a visualization within the same interface is remarkable.
Scaling up, GLM-4.5 tackles a 10-disc version, where instead of enumerating every move, it outlines a recursive algorithm in its chain of thought. The model breaks down the solution into levels, detailing the process of moving groups of discs step-by-step. This approach shows a deep understanding of the problemโs structure and an ability to abstract complex recursive patterns.
Interactive 3D and Visualization Projects
Beyond puzzles, GLM-4.5 has demonstrated impressive capabilities in generating interactive and visual content purely from prompts:
- Lego Building Simulation: Using three.js, GLM-4.5 created an interactive Lego building environment contained in a single HTML file. Users can add blocks of various sizes and colors, stacking them accurately. Although not perfect, the simulation is highly accurate and one of the best among AI-generated models.
- Solar System 3D Visualization: The model built a fully interactive solar system simulation with adjustable settings such as rotation speed, orbit visibility, planet size, lighting, and tooltips. This visualization is not only visually impressive but educational, offering users detailed information about each planet and the dynamics of the solar system.
- Game and UI Simulations: Other demos include a playable Flappy Bird game, a 3D maze explorer with realistic lighting and shadows, a to-do board for task management, SVG animations showing the evolution of language models, and even a Pokedex with detailed stats and images of Pokรฉmon.
These demos underscore GLM-4.5โs versatility and demonstrate how it can be used not only for reasoning and coding but also for creative and interactive applications.
๐ Benchmarking GLM-4.5: Where Does It Stand?
Performance benchmarks are crucial for understanding how GLM-4.5 stacks up against other models in the AI ecosystem. Matthew Berman highlighted several benchmark results that place GLM-4.5 at or near the top of the open-source pack.
General Benchmark Scores
On broad benchmarks, GLM-4.5 scores a 63.2 index, just behind the top open-source models like GPT-3 (o3) at 65 and Grok-4 at 63.6, but above Claude 4 Opus. The smaller GLM-4.5 AIR model scores 59.8, which is impressive given its size.
Agentic and Reasoning Benchmarks
GLM-4.5 outperforms Grok-4 on agentic benchmarks such as Tau Bench, BFCLV3, and Browse Comp, which evaluate the modelโs ability to use tools and perform agentic tasks.
In reasoning benchmarks like MMLU Pro, AMY 2024, and Math 500, GLM-4.5 performs very well, surpassing Claude 4 Opus but trailing behind some of the top-tier models like DeepSea Car 1 and Gemini 2.5 Pro. These results are significant because they show that GLM-4.5 is not just competitive but a leader among open-source models for complex reasoning tasks.
Coding Benchmarks
In coding-specific benchmarks, GLM-4.5 continues to shine, competing closely with other frontier models. While some closed-source models like Claude dominate the top spots, GLM-4.5โs coding capabilities are clearly state-of-the-art for an open-source offering.
Efficiency and Model Size
One of the standout points is GLM-4.5โs efficiency relative to its size. Compared to Kimi K2, a recently released model with similar quality, GLM-4.5 achieves comparable results with less than half the parameter count. This efficiency is vital for practical deployment and scalability.
๐ง Hybrid Reasoning and Thinking Modes
A unique aspect of GLM-4.5 is its hybrid reasoning design. The model can switch between a โthinking modeโ for complex reasoning and tool use, and a โnon-thinking modeโ for quick, straightforward responses. This dual-mode capability allows the model to optimize for different tasks, balancing speed and depth of understanding.
Interestingly, during Matthewโs tests, even simple questions like โWhat is the capital of California?โ or โTell me a storyโ triggered the thinking mode, suggesting that the model defaults to deeper reasoning for most queries. This behavior indicates a strong emphasis on accuracy and thoughtful responses, though future updates may refine this to better differentiate task complexity.
๐ Why GLM-4.5 Matters: The Rise of Chinese Open-Source AI
The release of GLM-4.5 is more than just a technical milestone; it signals a shift in the global AI landscape. Chinaโs open-source AI initiatives are rapidly closing the gap with Western closed-source giants, democratizing access to cutting-edge AI technology.
For developers, researchers, and businesses, this means more freedom to customize, experiment, and build on top of powerful models without restrictive licenses or black-box limitations. Open-source models like GLM-4.5 foster innovation by enabling collaboration and transparency.
Moreover, GLM-4.5โs agentic capabilities and reinforcement learning post-training highlight that open-source models can be fine-tuned for sophisticated, autonomous tasks that were once thought exclusive to proprietary systems.
๐ Practical Applications and Use Cases
Given its reasoning and coding strengths, GLM-4.5 is well-suited for a broad range of applications, including but not limited to:
- Advanced Coding Assistants: Automating code generation, debugging, and complex algorithmic problem-solving.
- Educational Tools: Interactive simulations like the solar system visualization or puzzle solvers can be integrated into learning platforms.
- Game Development: Creating AI-driven game logic, level design, and interactive environments.
- Research and Development: Conducting experiments in natural language reasoning, multi-step problem solving, and AI-driven creativity.
- Agentic AI Systems: Building autonomous agents capable of tool use and decision-making in dynamic environments.
As the open-source ecosystem around GLM-4.5 grows, we can expect even more innovative applications to emerge.
๐ Bonus: Free AI Resources Recommended
For those eager to deepen their understanding of AI models, prompts, and practical tools, Matthew Berman recommends an excellent free resource sponsored by HubSpot: the AI Decoded Guide. This comprehensive pocket guide covers everything from choosing the best models for creative writing or coding to crafting effective prompts and exploring custom GPTs for various use cases.
Whether youโre a beginner or an experienced AI practitioner, this guide is a valuable asset to help you navigate the rapidly evolving AI landscape. You can download it for free and start leveraging AI more effectively in your projects.
โ Frequently Asked Questions (FAQ) about GLM-4.5
What is GLM-4.5?
GLM-4.5 is an advanced open-source AI model developed by z AI in China. It is designed for both reasoning and coding tasks, featuring a mixture of experts architecture and hybrid reasoning capabilities.
How does GLM-4.5 compare to closed-source models?
GLM-4.5 performs on par with many leading closed-source models in benchmarks related to reasoning, coding, and agentic tasks. It offers competitive accuracy and efficiency while being fully open source.
What are the main applications of GLM-4.5?
GLM-4.5 can be used for coding assistance, puzzle solving, educational simulations, game development, and building autonomous AI agents capable of tool use.
Is GLM-4.5 available for public use?
Yes, GLM-4.5 is open source and available on platforms like Hugging Face. Developers and researchers can access and implement it in their projects.
What makes GLM-4.5โs architecture unique?
It uses a mixture of experts model, activating subsets of parameters dynamically, and offers a hybrid reasoning mode that switches between deep thinking and fast responses depending on the task.
Does GLM-4.5 support reinforcement learning?
Yes, it incorporates post-training with reinforcement learning to enhance its agentic capabilities, allowing it to perform tool use and autonomous decision-making more effectively.
How does GLM-4.5 handle complex puzzles like Tower of Hanoi?
The model breaks down recursive problems into logical steps or algorithms within its chain of thought, allowing it to solve puzzles purely through reasoning and even visualize the solution.
Where can I learn more about AI models and prompts?
HubSpotโs free AI Decoded Guide is an excellent resource that covers various models, prompt engineering, and practical AI applications. It is highly recommended for expanding your AI knowledge.
๐ฎ Conclusion: A Bright Future for Open-Source AI
GLM-4.5 is a landmark achievement in the AI world, especially for the open-source community. Developed in China, it demonstrates that open-source models can not only keep pace with but also challenge closed-source giants in tasks ranging from coding and reasoning to agentic behavior.
Its impressive demos, including Rubikโs Cube solving, Tower of Hanoi puzzles, interactive 3D visualizations, and game simulations, showcase the modelโs versatility and practical potential. The strong benchmark performances further cement its status as a frontier model, capable of driving innovation across industries.
As AI continues to evolve, models like GLM-4.5 will play a crucial role in democratizing access to advanced technology, sparking creativity, and enabling new applications that were once the domain of only the largest tech companies. For developers, researchers, and AI enthusiasts, this is an exciting moment to explore, contribute to, and benefit from the open-source AI revolution.
Stay tuned for upcoming models like GPT-5, which promise to push the boundaries even further. Meanwhile, GLM-4.5 stands as a powerful testament to the incredible progress happening right now in open-source AI.
