Gemini 3 Pro vs Flash: The AI Model Showdown for Vibe Coding

Exploring Vibe Coding with Gemini Models

The concept of Vibe Coding resembles a conversation with an AI chatbot to develop an application. While the process seems straightforward, it requires time and perseverance to overcome technical hurdles. I undertook several projects in this area, constantly seeking to evaluate the quality of the results obtained, particularly based on the AI model used.

With a multitude of AI models available, the results can vary significantly, especially if one does not have a well-defined plan. My goal was to compare lighter models to what are referred to as "thinking" models, as designated by Google and OpenAI. These lighter models have various names: at Google, the Gemini interface refers to them as Fast, although the model is specifically called Gemini 2.5 Flash, while OpenAI refers to them as Instant.

For this experiment, I chose to undertake a project using two distinct models. I first designed a complete project with Gemini 3 Pro, then attempted to replicate it with a lighter model, namely Gemini 2.5 Flash. The results were revealing: although both models produced a similar outcome, the approaches to achieve it were fundamentally different.

Lacking inspiration for this test, I asked Gemini to suggest interesting vibe coding projects. I opted for a project titled "Trophy Display Case." I requested Gemini to replace the trophies with a list of horror movies, with additional information accessible by clicking on the posters. Beyond these guidelines, I left it open for the Gemini models to express their creativity.

Comparison of Fast and Thinking AI Models

Google offers a choice between Flash and Pro models, which implies substantial differences, right? The answer is both yes and no. Although both are large language models, their functioning diverges. For the average user, the terms "fast" and "thinking" suffice to illustrate the distinction: speed versus depth.

A reasoning model, or LLM, is designed to break down complex problems into simpler steps before generating a final result. This occurs through an internal reasoning pathway. The Gemini 2.5 Flash and Gemini 3 Pro models are reasoning models, but Gemini 2.5 Flash adopts a hybrid approach, balancing speed and reasoning.

Gemini 3 Pro is the most powerful reasoning model, optimized for deep exploration to find answers. As a result, it is slower than more efficient models like 2.5 Flash. Since then, Google has launched Gemini 3 Flash, a more powerful base model that replaces 2.5 Flash. Gemini 3 Pro remains the most powerful reasoning model available in Gemini for most users.

Results of the Project with Gemini 3 Pro

The final project produced by Gemini 3 Pro was not perfect, but it exceeded my original idea and was significantly better than what Gemini 2.5 Flash had produced. Thanks to Gemini 3 Pro, I was able to create a homepage showcasing the movies from my list, complete with poster images, and by clicking on a title, a page would open to reveal additional information, as well as a link to watch the trailer on YouTube. Although the project was not complex, I encountered numerous problems and errors along the way.

Initially, I wanted to embed the trailers directly on the page, but this generated errors that Gemini could not correct, leading me to opt for a linked image to YouTube. While this was acceptable, it was not as seamless as I had hoped. However, I appreciated how Gemini 3 Pro detailed the specific issues encountered with this feature, allowing me to decide to abandon it.

Another issue that Gemini 3 Pro attempted to resolve multiple times concerned what it described as an overlay problem. When clicking on a poster, a pop-up window with the movie details would appear, accompanied by a small button to exit this view, but it never worked. I asked Gemini to fix it four times, and it only managed to resolve the issue on the last request. Gemini explained what it was doing with the code in general terms but never went into too much detail, although I imagine it would have provided specifics if I had asked.

The original project simply aimed to display a collection of movies and obtain more information about them. Beyond that, I had not thought about formatting or ways to make the web application interesting, and Gemini 3 Pro was helpful in this area. When I asked how I could improve the application, both in design and functionality, it suggested adding a 3D wheel effect to the movies and a random selection option.

This project required nearly 20 iterations. The final product was about as good as it could be, and it was a fun project, but there were issues that Gemini failed to correct more often than not. The final product exceeded my expectations, so I was satisfied.