Vibe coding can be an exciting process, allowing users to create apps with AI models like Google’s Gemini and OpenAI’s models. Exploring the differences between “thinking” models, like Gemini 3 Pro, and “fast” models, such as Gemini 2.5 Flash, reveals distinct workflows. In an experiment, creating a movie display app with both models showed that while both produced similar outputs, their approaches differed significantly. Gemini 3 Pro facilitated smoother coding with comprehensive solutions, automating several tasks and clarifying issues. In contrast, Gemini 2.5 Flash often required manual intervention, struggling with broader task execution and providing instructions that were less intuitive. This model necessitated more specific prompts, making it feel more labor-intensive. With nearly 20 iterations for the project, Gemini 3 Pro ultimately delivered a more efficient experience, showcasing its depth over speed. Thus, understanding these differences is crucial for optimizing vibe coding projects.
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