Figma to Code in 2026: Every Option Compared

The promise of "design to code" has been around for years. Press a button, get production-ready code. We're not there yet, but we're closer than ever.

Here's the landscape of Figma-to-code options in 2026, what actually works, and when to use each approach. For a broader view of AI design tools beyond just code conversion, check out our comparison of 15 AI app design tools.

Skip the conversion step entirely: Generate designs and code together with text-to-app — no Figma required, no conversion needed.

The options at a glance

Manual developer handoff

The traditional approach: designers create specs, developers write code.

How it works:

1. Design in Figma
2. Add annotations, specs, and component documentation
3. Share design link with developers
4. Developers inspect and rebuild in code

Pros:

• Best code quality (developers write it how they want)
• Handles complex interactions properly
• Works with any tech stack
• No tool lock-in

Cons:

• Slow (developer time is expensive)
• Potential for interpretation differences
• Back-and-forth on implementation details

When to use it: Production apps where code quality matters. Complex interfaces with lots of interaction. Teams with dedicated developers who know the codebase.

Tools that help:

• Figma Dev Mode (inspect panel, code snippets)
• Zeplin (dedicated spec tool)
• Storybook (component documentation)

Figma Dev Mode

Figma's built-in feature for developers. Shows CSS properties, dimensions, and component info.

What it provides:

• CSS code snippets for selected elements
• Spacing and dimension values
• Color values and gradients
• Typography specs
• Asset export

What it doesn't provide:

• Complete, runnable code
• Proper HTML structure
• Component relationships
• Responsive behavior

The reality: Dev Mode is a reference tool, not a code generator. Developers still write everything from scratch; they just have accurate specs to work from.

Pricing: Requires Figma paid plan. Dev Mode seats are separate from design seats.

Export plugins

Figma plugins that export designs to code. Quality varies wildly.

Anima

Converts Figma to HTML, React, or Vue.

Pros:

• Reasonably good static output
• Handles basic responsiveness
• Supports animations (in their tool)

Cons:

• Code is verbose and not how you'd write it manually
• Complex layouts often break
• Needs cleanup before production use

Pricing: Free tier limited. Paid plans from $39/month.

Locofy

Positions itself as production-grade design-to-code.

Pros:

• Attempts to create reusable components
• Supports React, Next.js, Gatsby, React Native
• Better handling of complex layouts than competitors

Cons:

• Requires "tagging" elements in Figma (extra work)
• Still needs significant developer cleanup
• Struggles with responsive design

Pricing: Free tier available. Pro from $25/month.

Builder.io Figma plugin

Exports to Builder.io's visual editor format, then to various frameworks.

Pros:

• Good for content-driven pages
• Works with their CMS
• Ongoing visual editing after export

Cons:

• Tied to Builder.io ecosystem
• Not ideal for complex app UIs
• Learning curve for the platform

Pricing: Free tier. Paid plans from $19/month.

HTML/CSS exporters (various)

Simpler plugins that export static HTML and CSS.

Pros:

• Quick and simple
• Good for landing pages
• No subscriptions for basic functionality

Cons:

• Static only (no interactivity)
• Code quality varies
• Not component-based

Best for: One-off landing pages, email templates, quick prototypes.

AI conversion tools

The new category. AI that understands designs and generates code.

Screenshot-to-code AI

Tools like screenshot-to-code, Vercel's v0, and similar.

How they work:

1. Upload a screenshot or describe what you want
2. AI generates code (usually HTML/Tailwind or React)
3. Iterate through conversation

Pros:

• Fast (minutes, not hours)
• Understands intent, not just pixels
• Can generate from rough sketches or descriptions
• Iterative refinement

Cons:

• Doesn't preserve exact design specs
• May interpret designs differently than intended
• Not connected to your actual Figma file

When to use: Starting points, MVPs, exploring possibilities.

GenDesigns approach

We do things differently—generate the design AND the code together.

How it works:

1. Describe your app in plain language
2. AI generates visual designs
3. Export includes both images and HTML/Tailwind code

Why this might be better: No translation step between design and code. They're created together, so they match perfectly.

Trade-off: You're not starting from a Figma file. If your workflow requires Figma for collaboration, this is a different path entirely. To understand when each approach makes sense, see our Figma vs AI app builder comparison.

Skip Figma entirely

Worth mentioning: you don't have to start in Figma.

Code-first prototyping

Some teams prototype directly in code:

• Storybook for component development
• Tailwind UI for pre-built components
• Vercel's v0 for AI-generated UIs

Pros:

• No design-to-code translation needed
• Prototype is the actual product
• Developers stay in their environment

Cons:

• Harder for non-developers to participate
• Visual exploration is slower
• Harder to get stakeholder feedback

AI-first design

Start with AI design tools, get code from the beginning — this is the text-to-app approach:

• GenDesigns (mobile app focus)
• Vercel v0 (web components)
• Galileo AI (general UI)

Pros:

• Fastest path from idea to code
• No learning curve for design tools
• Accessible to non-designers

Cons:

• Less control over exact design decisions
• May not match existing brand systems
• Iterating on small details can be tedious

What "good code" actually means

The promise of design-to-code tools is "production-ready code." But what does that mean?

Clean code structure: Logical HTML hierarchy, proper semantic elements, CSS that makes sense.

Component-based: Reusable pieces, not copy-pasted styles everywhere.

Responsive: Works on different screen sizes without breaking.

Accessible: Proper ARIA labels, keyboard navigation, contrast ratios.

Performant: Reasonable file sizes, optimized images, efficient CSS.

Maintainable: Another developer can understand and modify it.

Current tools handle the first one or two. Few handle all of them. Manual development is still the only way to get truly production-ready code in most cases.

Realistic workflow recommendations

For startups and small teams

Phase 1: Validate Skip Figma. Use AI tools to generate designs and code quickly. Test with users. Iterate.

Phase 2: Refine Once you know what to build, either:

• Clean up AI-generated code manually
• Rebuild with proper architecture

Why: Speed matters more than code quality when you're still figuring out what to build.

For established products

Option A: Traditional handoff Design in Figma. Use Dev Mode for specs. Developers code from scratch.

Option B: AI-assisted development Design in Figma. Use AI to generate first-pass code. Developers refine.

Why: You have brand systems, component libraries, and architectural decisions that matter. Tools can assist but can't replace thoughtful implementation.

For agencies and client work

For pitches and concepts: AI-generated designs and code. Fast, impressive, disposable.

For production work: Traditional handoff. Clients expect quality code they can maintain.

The honest truth about design-to-code

Every few years, a tool claims to solve design-to-code. None have fully delivered. Here's why:

Design and code serve different purposes. A design communicates visual intent. Code creates functional interfaces. Translating one to the other requires decisions that tools can't make well.

Edge cases are infinite. What happens on a tiny screen? With really long text? When the API fails? Designs don't capture these. Code must handle them.

Code quality is subjective. Different teams have different standards, architectures, and preferences. Automated code can't match your specific conventions.

But things are getting better. AI understands intent, not just pixels. Component-based design tools are more structured. The gap between design and code is shrinking.

My prediction: within a few years, AI will generate code good enough for 80% of use cases. The other 20% will still need skilled developers.

Try before you commit

Every tool mentioned here has a free tier or trial. Before committing to a workflow:

1. Take a real design from your work
2. Run it through 2-3 different tools
3. Have a developer evaluate the output
4. Estimate cleanup time vs manual coding time

The right answer depends on your specific situation, team skills, and quality requirements.

Related reading:

• Figma vs AI App Builder - Which approach is better?
• 15 Best AI App Design Tools in 2026 - Complete tools comparison
• Best React UI Components for Mobile in 2026 - Component libraries