Fast vs. Thinking Gemini Models: A Vibe‑Coding Comparison

A hands‑on experiment compared Google’s Gemini 3 Pro (a “thinking” model) with Gemini 2.5 Flash (a “fast” model) for vibe‑coding—a workflow that creates web projects through natural‑language prompts. Using the same project idea, a horror‑movie showcase, the author found the Pro model produced a more polished result with fewer manual steps, while the Flash model was quicker but required more specific prompting and frequent fixes. The test highlighted differences in speed, depth of reasoning, and user effort, offering insight for developers choosing between Gemini’s model tiers.

Experiment Overview

The author set out to compare two tiers of Google’s Gemini large‑language models by building the same web‑app project through natural‑language interaction, a practice known as vibe‑coding. The project involved displaying a list of horror movies, showing poster images, and providing additional details on demand. The same prompts were used with Gemini 3 Pro, described by Google as a “thinking” model, and Gemini 2.5 Flash, labeled as a “fast” model.

Model Characteristics

Both models are reasoning‑capable, but Gemini 3 Pro is optimized for deeper, step‑by‑step problem solving, which makes it slower yet more thorough. Gemini 2.5 Flash balances speed with reasoning, offering quicker responses at the cost of occasional shortcuts and a need for more precise prompting.

Results with Gemini 3 Pro

The Pro model generated a functional landing page that displayed movie posters, linked to YouTube trailers, and opened a details view when a poster was clicked. Although some features—such as embedding trailers directly—proved problematic, the model identified the issues and allowed the author to decide on alternative solutions. The Pro model also suggested design enhancements like a 3D wheel effect and a random‑pick option, and it handled the integration of an API key for The Movie Database without explicit instruction. Overall, the final product required fewer manual interventions and delivered a more cohesive experience.

Results with Gemini 2.5 Flash

The Flash model produced a workable version more quickly, but it often suggested manual steps, such as acquiring images manually, and required the author to replace specific code sections rather than providing a full rewrite. When asked to incorporate the same API key, the model added it but failed to pull correct poster images, resulting in many mismatches. The model also expressed limitations, noting that locating exact movie IDs would be time‑consuming. The final output contained numerous errors that persisted despite multiple correction attempts.

Key Differences in Workflow

With Gemini 3 Pro, each adjustment triggered a full code rewrite, allowing the author to copy‑paste the entire updated file without tracking individual changes. In contrast, Gemini 2.5 Flash often returned only the modified snippet and instructed the user to swap it into the existing code, a process that could stall users unfamiliar with the codebase. The Pro model also took the initiative to suggest external resources and design improvements, whereas the Flash model waited for explicit prompts.

Takeaways

The experiment demonstrated that the “thinking” model delivers higher‑quality results with less hands‑on troubleshooting, albeit at a slower pace. The “fast” model offers speed but demands more precise instructions and greater user involvement to correct shortcuts and errors. Users seeking a smoother vibe‑coding experience may prefer Gemini 3 Pro, while those prioritizing rapid iteration and willing to manage additional manual steps might opt for the Flash model.