The video reviews the GLM4.5 open-weight AI model, praising its effectiveness and speed for small to medium coding tasks, especially UI design, while noting limitations like a relatively small context window and higher costs. The presenter shares practical experiences, highlights the model’s strengths in interpreting unclear requirements, and recommends it for focused coding projects, encouraging community feedback and exploration of related GLM4.5 models.
The video discusses the experience of using the GLM4.5 open-weight AI model for programming tasks, highlighting its respectable position in the top 10 most used models on Open Router. The presenter shares that despite previously favoring another model, Quint3 coder, they decided to dedicate a full day to using GLM4.5 exclusively in their production codebase. They found the model pleasant to work with, noting minimal tool failures and good speed and latency. However, the main limitation identified was the relatively small context window of 131,000 tokens, which restricts the model’s effectiveness to smaller, more contained coding tasks rather than large-scale refactoring or multi-file projects.
Throughout the day, the presenter made 21 commits across 27 files, mostly involving bug fixes, UI improvements, and some backend work. They praised GLM4.5’s ability to interpret unclear human language requirements effectively, which helped clarify a poorly written ticket. The model performed well with medium reasoning settings, which improved its output quality compared to lower reasoning configurations. Despite some challenges with client-side tools due to context window limitations and provider inconsistencies, the model worked well in environments like Root Code and Open Code, especially when prompt compression was enabled to manage context size.
Cost was a significant consideration, as the presenter burned through approximately 31 million tokens over multiple days, spending around $19. This expense is relatively high for daily coding tasks, especially when compared to cheaper alternatives like Cloud Code. The presenter emphasized that keeping chat threads short and avoiding long chains is crucial to maintaining cost-effectiveness with GLM4.5. They also noted that prompt compression, while necessary to fit within the context window, might interfere with prompt caching, potentially increasing costs for longer tasks.
The video also showcased several practical applications built using GLM4.5, including a visually appealing loading animation with tetraminos, a real-time face filter app with various fun effects, and a 3D autonomous drone simulation. The model excelled at front-end UI tasks, which the presenter, not being a UI specialist, found particularly valuable. GLM4.5 helped generate clean, functional UI components and email templates, demonstrating its strength in design and user interface development. The presenter expressed enthusiasm about using the model for small UI projects and appreciated having multiple AI models to choose from for different aspects of their work.
In conclusion, the presenter highly recommends GLM4.5 for small, focused coding tasks, especially those involving UI design, due to its quality and usability. They acknowledge its limitations with larger context needs and cost but find it a valuable tool in their AI coding toolkit. The video ends with a call for community feedback on experiences with GLM4.5, mentions interest in the GLM4.5 Air and vision models, and encourages viewers to try the model for light coding tasks. Overall, the video provides a balanced and practical review of GLM4.5’s capabilities and trade-offs in real-world programming scenarios.