Which Vibe Coding Tool Should You Use: A Practical Comparison of v0, Bolt, Lovable, and Replit
I wanted to figure out which of the main vibe coding tools, v0, Bolt, Lovable, and Replit, work best in different scenarios.
I wanted to figure out which of the main vibe coding tools, v0, Bolt, Lovable, and Replit, work best in different scenarios. To find out, I ran a test.
I gave each tool the same prompt: Build a web app that allows me to input a URL or upload a screenshot and then make variations to it for an A/B test.
After that initial prompt, I tailored follow up instructions based on each tool’s output to see how far you can get with targeted prompting and a little familiarity with how each tool behaves.
The Task
The prototype I wanted was simple in concept:
Paste a URL or upload a screenshot
Visually select elements (like buttons or headlines)
Change styles or text
Save those variations for testing
The goal was to streamline internal experimentation with a lightweight A/B testing editor.
How Each Tool Performed
v0
It finished first, producing a barebones design quickly. But the app didn't work.
It removed the URL feature entirely and focused on image upload, which also didn't work.
When prompted about the broken functionality, it responded with a six-month roadmap, which didn't help fix the immediate issues.
Eventually, it got stuck trying to write code and couldn’t recover without additional direction.
Takeaway: It's good for quick UI sketches or mockups, but it tends to stall when the task requires thinking beyond the screen.
Bolt
The first version had a well-structured, multi-screen interface, and the UI looked polished.
Neither the URL nor image uploader worked, and both attempts led to a broken screen.
In later prompts using the “Discuss” feature instead of “Build,” it came closer to understanding what was needed.
It appeared confident, but ultimately built the wrong things and couldn't get past the dummy image. It did better after restarting with more detailed instructions.
Takeaway: It's solid for prototyping user flows, but it needs more direction to deliver working features.
Lovable
It broke early in the build, but eventually fixed itself and continued working.
It didn’t build a working version, but got close after two prompts. The app remained mostly a shell with no real functionality.
Despite that, it correctly inferred the need for a CORS solution and even asked to be prompted for refactoring, which showed a surprising level of self awareness.
After two hours of testing, the visual editor still wasn’t working, but Lovable had tackled more technical nuance than the others.
Takeaway: It's more intuitive than the others and shows potential when paired with clearer prompts and more iteration.
Replit
Neither the URL input nor image upload worked initially.
When prompted to explain what was wrong, it offered a clear explanation of what needed to change and how to move forward.
It got to a working version fairly quickly after that, successfully pulling in the page with an iframe.
It still didn’t create variations, but the iframe integration and editor loading were functional and stable.
Takeaway: It's slower but more thorough, with stronger technical grounding. It's especially useful if you can write or interpret specs.
Observations
Prompting Strategy Matters
The tools worked better when:
Prompts included clear technical details
Instructions were step by step
Using ChatGPT as a Senior Dev
One approach that worked especially well was treating the AI’s output like it came from a junior developer, and then using ChatGPT as the senior engineer in the room.
After getting a partial or broken response from an editor, I’d summarize what it was trying to do, then give those notes to ChatGPT with this prompt:
"Assume these are notes from a junior dev. Act like a senior dev and write instructions back to them."
ChatGPT would respond with more structured, technically grounded directions. This process helped clarify the architecture, identify missing pieces, and improve the quality of my next prompt to the editor. It also helped me catch when I was being too vague or assuming the tool would fill in the gaps. In short, it created a loop where I could use one AI to upgrade the output of another.
ChatGPT was useful for translating fuzzy goals into specific tech specs, which made a big difference, until the details overwhelmed the editors.
Quick Comparison
Final Notes
None of these tools just work. But they're each useful in the right context. What matters is how well you can guide them, and how well you understand the problem you're trying to solve.
If your task is well structured and you're clear with your inputs, these editors can accelerate your workflow and save time on early stage builds. They're not a replacement for engineering yet, but they're a great extension of your thinking process.
If you're experimenting with vibe coding tools, or want to build internal prototypes without spinning up a full dev team, this kind of testing can help you figure out where each tool fits.