Horizon-Beta vs. GLM-4.5 vs. Codestral-2508 & More | Advanced RAG + HTML Generation

In this video, I'm putting five powerful Large Language Models—Horizon-Beta, GLM-4.5, Codestral-2508, Qwen3-Coder, and Qwen3-30b—to the ultimate test! I'm not just asking them to write text; I'm challenging them to act as "elite full stack developers." Using the free and open-source MAIAChat-Desktop application, I've built a Retrieval-Augmented Generation (RAG) system with the gemini-embedded-001 model, feeding it several large PDF documents. My mission? To see which LLM can take the retrieved data and create the most visually stunning, interactive, and detailed website—all in a single HTML file! You'll see exactly how each model handles a complex, multi-part prompt that demands creativity, data accuracy, and technical skill, including: Extracting specific figures and details from a knowledge base about global growth and demographics. Creating a fully functional, single-file HTML website with CSS, JavaScript, interactive elements, and animations. Implementing data export features for PDF, PNG, and CSV formats. Thinking creatively to build a truly outstanding and visually appealing page. I’ll break down the code and the final results, comparing the pros and cons of each model's output. I think you'll be surprised by which one comes out on top! Get the tool: MAIAChat-Desktop: https://github.com/alexcelewicz/MAIAChat-Desktop