The video compares Codeex with GPT-5 and Claude Code with Opus Sonnet by having both AI models develop a comic strip generator app from the same detailed PRD, highlighting their respective strengths in speed, UI design, and architectural approach while addressing minor issues encountered. It concludes that neither model is definitively superior, emphasizing the importance of clear, modular prompting and ongoing refinement, and invites community collaboration for further exploration.
The video presents a detailed comparison between two AI coding assistants: Codeex with GPT-5 and Claude Code with Opus Sonnet. The host begins by setting up a fair test environment using a Product Requirements Document (PRD) generated by Gemini 2.5 Pro Deep Think. This PRD outlines a project to build a character comic strip generator app that integrates the replicate ideogram API, involving both backend and frontend components. Both AI models are tasked with developing the same application based on this PRD, allowing for a direct comparison of their coding capabilities, speed, and output quality.
Throughout the development process, the host observes that both Codeex with GPT-5 and Claude Code with Opus Sonnet perform well, each with their strengths. Codeex is noted for its faster execution and more interactive UI, while Claude Code offers a cleaner user interface and better architectural design. Both models successfully generate the three-panel comic strip app, maintaining character consistency and handling image inputs effectively. However, minor issues such as API authentication errors and prompt precision are encountered and addressed during testing.
The video also delves into the nuances of AI model behavior, highlighting that GPT-5 tends to follow explicit instructions very closely, which can be both an advantage and a limitation. Claude Code and Opus Sonnet demonstrate stronger domain expertise and architectural adherence but sometimes make autonomous decisions that deviate from strict instructions. The host emphasizes the importance of providing high-signal, concise context to AI models to improve output quality and reduce errors, suggesting that modular, step-by-step prompting is more effective than overwhelming the model with extensive context at once.
Further, the host reviews a third-party video analyzing similar AI models, reinforcing the findings that while GPT-5 has improved significantly in instruction following and engineering output, it still requires explicit guidance and iterative refinement. The video underscores that none of the models can perfectly execute complex projects in a single shot, and ongoing debugging and prompt adjustments remain essential. The discussion also touches on the evolving landscape of AI coding tools, subscription costs, and the potential impact of emerging models like Gemini 3 on pricing and competition.
In conclusion, the host expresses a balanced view, appreciating the advancements in both Codeex with GPT-5 and Claude Code with Opus Sonnet while acknowledging their respective limitations. The comparison reveals that neither model is definitively superior; instead, their effectiveness depends on the use case, prompting style, and user preferences. The video ends with plans to explore community collaboration, live streams, and further iterations on the tested app, inviting viewers to share insights and contribute to ongoing AI coding discussions.