Let me start with a confession: I've wasted more hours than I'd like to admit manually converting Figma designs into React components. Pixel-pushing CSS at 11pm while wondering why the designer added seventeen slightly different shades of gray. If that pain sounds familiar, you're going to love where things have landed in 2025.
Figma to code AI tools have finally grown up. They're not perfect—and I'll tell you exactly where each one falls short—but they're now genuinely useful for real production work. Not just toy demos.
So let's cut through the marketing fluff and look at what actually works.
Why 2025 Is Different for Design-to-Code
Here's what changed: the underlying AI models got dramatically better at understanding design intent, not just visual layout. Earlier tools could export your Figma frame as code, sure. But the output read like a robot had a nervous breakdown—absolute positioning everywhere, zero semantic structure, class names that would make your team lead cry.
The new generation actually understands things like "this is a navigation bar" or "these cards should probably be a mapped component." That shift from pattern-matching to genuine comprehension is why these tools suddenly became worth your time.
But here's the thing nobody tells you: picking the right tool matters more than the tool's raw capabilities. A tool that outputs Vue when you're a React shop is useless. One that generates beautiful code but doesn't handle your design system is a nightmare. Context is everything.
What Actually Matters in a Figma to Code Tool
Before we dive into specific tools, let's talk about what you should actually evaluate. I've seen too many developers pick tools based on flashy demos that fall apart on real projects.
Framework support: Does it output React, Vue, Angular, or plain HTML? Can it use Tailwind or does it generate inline styles?
Component intelligence: Can it recognize reusable patterns? Does it create actual components or just one giant blob of markup?
Design system integration: Can you map your Figma components to your existing component library?
Responsiveness: Does it handle mobile layouts, or do you need to fix everything manually?
Export quality: Is the code actually readable? Would you be embarrassed to commit it?
Now let's see how the contenders stack up.
Figma Make: Figma's Native Solution
Figma Make is Figma's own answer to the "export to code" problem, and it's got some serious advantages just by being built into the tool you're already using.
What it does well:
The integration is seamless—no plugins, no copying tokens, no hoping the connection doesn't break. You design in Figma, you generate code in Figma. For simple components and quick prototypes, the workflow is surprisingly smooth.
It also benefits from Figma's deep understanding of your design file structure. Auto-layout elements translate reasonably well to flexbox. Component instances stay linked. The fundamentals work.
Where it falls short:
Honestly? It's still pretty basic for production work. The generated code tends toward verbosity, and the component recognition could be smarter. It feels like Figma built something adequate to check a box rather than something that would blow your mind.
Pricing is also a consideration—Make is bundled into Figma's premium tiers, so you're already paying for it if you're on a professional plan. That's either a pro or a con depending on your situation.
Verdict: Great for simple exports and prototyping. Not yet ready to replace your hand-coded component library.
Builder.io Visual Copilot: The Multi-Framework Powerhouse
Builder.io's Visual Copilot is probably the most ambitious tool in this space. They're not just doing Figma to code—they're trying to be the universal translator between design and any frontend framework you can name.
What it does well:
The framework coverage is genuinely impressive. React, Vue, Svelte, Angular, Qwik, plain HTML—you name it, it probably supports it. And the Tailwind output is actually clean, which is rare.
But the killer feature? You can map Figma components to your own component library. So instead of generating a generic button, it can output <Button variant="primary" /> using your actual components. That's the difference between usable code and "I might as well rewrite this."
Where it falls short:
The learning curve is real. Visual Copilot is powerful, but that power comes with complexity. Setting up component mappings takes time upfront. If you're just trying to quickly export a one-off design, faster options exist.
Also, the free tier is limited. For serious usage, you're looking at paid plans—though for teams doing frequent Figma-to-code work, the time savings probably justify it.
Verdict: Best choice for teams with established design systems who need production-quality output. Overkill for casual use.
Locofy: Pixel-Perfect React Generation
Locofy has carved out a niche as the tool for developers who are obsessive about output quality. Their pitch is simple: we generate code that actually looks like a human wrote it.
What it does well:
The React output is genuinely impressive. Clean component structure, sensible prop names, responsive by default. They've clearly spent time understanding what "good" React code looks like and training their system to match.
Their auto-responsiveness feature is legitimately helpful. Instead of generating desktop-only code and making you figure out mobile, Locofy analyzes your design and attempts to make it work across breakpoints automatically.
Where it falls short:
Locofy is React-focused. If you're building in Vue or Svelte, this isn't your tool. They support a few other frameworks, but React is clearly where they've invested the most effort.
The pricing structure can also feel aggressive for individual developers. It's positioned more for teams and agencies than solo builders.
Verdict: If you're a React shop and code quality is your priority, Locofy is hard to beat.
Anima: The API-First Approach
Anima takes a different approach than most competitors. Instead of just being a Figma plugin, they're building developer tooling with API access, CI/CD integration, and proper workflow support.
What it does well:
For teams with mature development processes, Anima fits in well. You can trigger exports from your build pipeline, version your outputs, integrate with your existing tools. It feels less like a design tool and more like developer infrastructure.
Their Storybook integration is also solid—you can export Figma components directly to Storybook stories, which is genuinely useful for maintaining design system documentation.
Where it falls short:
The focus on enterprise workflows means the casual experience isn't as polished. If you just want to drag in a frame and get code, simpler options exist.
Code quality is good but not exceptional. You'll likely need some cleanup, especially for complex layouts.
Verdict: Best for teams with mature development workflows who want to integrate design-to-code into their pipeline.
Emerging Players Worth Watching
A few other tools are making noise in this space:
Bolt AI is generating buzz for its speed and simplicity. It's newer and less proven, but early reports suggest it handles basic layouts well.
CodeSpell AI is positioning itself as the enterprise choice with heavy emphasis on scalability and team features.
Relate focuses specifically on component recognition and design system mapping, which addresses one of the hardest problems in this space.
None of these have the track record of the tools above, but they're worth keeping on your radar as the market evolves.
Head-to-Head Comparison
Here's how the main tools compare across key dimensions:
| Feature | Figma Make | Builder.io | Locofy | Anima |
|---|---|---|---|---|
| React Support | Good | Excellent | Excellent | Good |
| Vue/Svelte Support | Limited | Excellent | Basic | Good |
| Tailwind Output | Basic | Clean | Clean | Good |
| Component Mapping | No | Yes | Limited | Yes |
| Responsiveness | Manual | Smart | Auto | Manual |
| Code Quality | Adequate | Very Good | Excellent | Good |
| Learning Curve | Easy | Moderate | Easy | Moderate |
| Pricing | Bundled | Freemium | Paid | Freemium |
Which Tool Should You Actually Choose?
Here's my honest recommendation:
Choose Figma Make if: You're already deep in the Figma ecosystem and just need quick exports for prototyping. The convenience factor is real.
Choose Builder.io if: You have an established component library and need production-ready output that uses your actual components. The mapping feature alone is worth the learning curve.
Choose Locofy if: You're React-focused and care deeply about code quality. The output really is noticeably cleaner than competitors.
Choose Anima if: You're part of a larger team with established DevOps practices and want design-to-code integrated into your pipeline.
And if none of these quite fit? That brings me to one more option.
Where Fardino Fits In
Here's something I've noticed: Figma to code AI tools are great when you have a finished, polished design. But what about when you're still exploring? When you want to quickly test an idea before investing in detailed Figma work?
That's where vibe coding tools like Fardino complement the workflow. Instead of starting in Figma, you can describe what you want—"a dashboard with a sidebar, user stats cards, and a recent activity feed"—and get a working React prototype in seconds.
If you've been learning vibe coding best practices, you know that the key is knowing when to use which tool. Figma-to-code works great for pixel-perfect implementations of approved designs. Vibe coding works great for rapid exploration and iteration.
They're not competitors—they're different tools for different phases of your workflow.
Want to try this yourself?
The Bottom Line
The Figma to code AI landscape in 2025 is actually usable now. Not perfect—you'll still need to review and refine the output—but genuinely helpful for cutting down tedious conversion work.
My advice? Don't overthink the initial choice. Pick one that supports your framework, has reasonable pricing for your usage, and try it on a real project. You'll learn more from 30 minutes of actual use than from reading another 10 comparison articles.
The tools are ready. The question is whether your workflow is ready to incorporate them.
If you're building frontends and haven't tried AI-assisted development yet, check out our guide on context engineering for AI coding to learn how to get better results from these tools. And if you're worried about making mistakes, our vibe coding mistakes guide covers the common pitfalls worth avoiding.
Now go ship something.
