I Tried Vibe Coding the Same Project Using Different Gemini Models. The Results Were Dramatic

Why It Matters

Choosing a reasoning‑focused LLM like Gemini 3 Pro can dramatically cut development time and improve code reliability, a critical advantage for teams adopting AI‑assisted coding workflows.

Key Takeaways

• Gemini 3 Pro delivers deeper reasoning, higher code quality
• Gemini 2.5 Flash is faster but needs precise prompts
• Pro rewrites full code; Flash gives snippets only
• API integration handled automatically by Pro, manually by Flash
• Project needed ~20 iterations with Pro, many fixes with Flash

Pulse Analysis

Vibe coding has emerged as a practical way for developers to prototype applications by conversing with large language models. The market now offers two distinct model families: "fast" variants optimized for latency and "thinking" models fine‑tuned for chain‑of‑thought reasoning. This dichotomy mirrors broader AI trends where speed often trades off with depth of understanding, influencing how quickly code can be generated and how robust that code will be. Understanding these categories helps organizations align model selection with project complexity and time‑to‑market goals.

Stimac's side‑by‑side test highlights the tangible impact of model choice on developer productivity. Gemini 3 Pro, the reasoning‑heavy option, consistently rewrote entire files, resolved UI bugs, and auto‑populated TMDB data, allowing the author to copy‑paste complete solutions. Gemini 2.5 Flash, while delivering faster responses, frequently returned isolated snippets, required explicit instructions for API integration, and left many visual glitches unresolved. The iterative effort with Flash escalated as the author had to manually stitch code fragments, illustrating how a model’s internal reasoning can reduce cognitive load and error‑prone hand‑editing.

For enterprises scaling AI‑augmented development, the experiment suggests a tiered strategy: reserve reasoning models like Gemini 3 Pro for complex, integration‑heavy projects where code quality and maintainability outweigh raw speed. Reserve fast models for simple scripts or rapid prototyping where developers can fill in gaps. As LLMs continue to evolve, hybrid approaches that blend speed with on‑demand reasoning may become standard, but today the clear productivity gains from deeper models make them a strategic asset for any tech‑forward organization.