For most teams, Anima is the safer choice for polished Figma-to-frontend work, while Locofy is better when you want more control over component structure and production-style React output. If your starting point is only a flat UI image, screenshot, or PNG mockup, be careful: no tool reliably turns that image into clean, ready-to-ship frontend code without human review.
TLDR: Anima works well for designers who want to export responsive React, HTML, or Vue from Figma with less setup. Locofy is stronger for teams that care about components, props, reusable layouts, and cleaner developer handoff. For example, a small SaaS team converting 12 landing page sections may save 30% to 50% of initial coding time, but should still plan several hours for cleanup, naming, responsiveness, and accessibility fixes.
What “image to code” really means
The phrase image to code sounds simple. Upload a UI image. Get frontend code. Ship it. Sadly, that is not how most real projects work.
A flat image has no layers, constraints, semantic meaning, reusable components, or state. The tool has to guess what is a button, what is a card, what is a heading, and what should resize. That guess can be impressive. It can also be wrong in deeply annoying ways.
Tools like Anima and Locofy perform best when they start from structured design files, especially Figma. They can read layers, frames, auto layout, text styles, and spacing. Screenshot-to-code products and AI systems try to infer those details from pixels, but the output usually needs heavier cleanup.
Anima: best for quick exports and designer-friendly workflow
Anima is a mature design-to-code tool focused on turning Figma, Adobe XD, and Sketch designs into frontend code. It supports HTML, React, Vue, and other outputs depending on workflow and plan. The strongest part is the interface. Designers can prepare screens, preview behavior, and export code without thinking like senior frontend engineers.
Anima is useful when speed matters. If a marketing team needs a landing page prototype coded quickly, Anima can create a solid first version. It usually preserves visual fidelity well. Spacing, colors, fonts, and layout often match the source file closely when the design file is clean.
Where Anima performs well:
- Visual accuracy: Good at matching the original design.
- Responsive setup: Helpful tools for breakpoints and flexible layouts.
- Designer handoff: Non-developers can produce previews and inspect code.
- Static pages: Strong for landing pages, portfolios, and marketing layouts.
The catch is that exported code may still feel generated. Class names can be generic. Component structure may not match your engineering standards. If your team uses strict design systems, custom hooks, state rules, or a house style for React components, expect cleanup.
Locofy: better for component thinking and developer control
Locofy is aimed more directly at frontend teams. It converts Figma and design files into React, Next.js, Gatsby, Vue, React Native, and HTML/CSS workflows. Its main value is not just “make this screen into code.” It helps map design elements to components and configure behavior before export.
Locofy often feels more useful when the product is bigger than a simple page. You can tag layers as buttons, inputs, lists, cards, or custom components. You can also define props and reuse UI blocks. That makes the exported code easier to fit into a real app.
Where Locofy performs well:
- Component mapping: Stronger control over what becomes reusable frontend code.
- Framework support: Good fit for React and Next.js projects.
- Developer handoff: More useful when engineers will maintain the code.
- Product interfaces: Better suited to dashboards, apps, and repeated UI patterns.
Honestly, it feels like Locofy asks for more setup than some teams expect. Tagging layers and defining structure takes time. But that time is not wasted if the goal is maintainable code instead of a quick visual clone.
Anima vs Locofy: practical comparison
| Category | Anima | Locofy |
|---|---|---|
| Best user | Designer, marketer, product team | Frontend developer, product engineer |
| Best use case | Landing pages and visual prototypes | Apps, dashboards, reusable components |
| Code control | Moderate | Higher |
| Setup effort | Lower | Higher |
| Cleanup needed | Medium | Medium, but often more structured |
If your goal is a quick page that looks close to the design, pick Anima. If your goal is code that can grow into a real product interface, pick Locofy. Neither tool removes the need for frontend judgment.
What about tools that turn screenshots into code?
There are also AI tools that accept screenshots or images and generate HTML, CSS, Tailwind, or React. Examples include v0 by Vercel, Uizard, Builder.io Visual Copilot, Fronty, and newer AI coding assistants that accept image prompts.
These tools can be useful for rough starts. Upload a screenshot of a pricing section, and an AI tool may produce a working Tailwind layout in under a minute. That is helpful. But it may also invent spacing, miss font weights, ignore accessibility, or turn every block into nested divs.
Expect to waste time on small but irritating fixes. A button may be 8 pixels too tall. A card grid may break on tablet widths. An icon may become a random SVG placeholder. These issues sound minor until they hit 40 components.
Other notable tools
- Builder.io Visual Copilot: Strong option for Figma-to-code workflows, especially with React and design systems. It aims for cleaner code than many visual exporters.
- v0 by Vercel: Very capable for generating React and Tailwind UI from prompts and images. Best for fast prototypes and shadcn-style interfaces.
- Uizard: Useful for early wireframes, mockups, and quick prototype generation. Better for ideation than final production code.
- TeleportHQ: Supports visual building and code export. Can help teams create static sites and simple frontend projects.
- Fronty: Focuses on image-to-HTML conversion. Good for experiments, but review the output carefully before using it in a serious project.
Code quality: the part vendors rarely stress
The real issue is not whether these tools can generate code. They can. The issue is whether the code is readable, accessible, responsive, and easy to change six months later.
A serious frontend project needs more than matching pixels. It needs semantic HTML, keyboard support, ARIA only where needed, sensible component names, optimized images, good loading behavior, and clean CSS. Generated code often passes the first visual check but fails deeper review.
For internal prototypes, that may be fine. For a customer-facing app, the bar is higher. A good rule is simple: use image-to-code tools to cut the first build phase, not to skip engineering.
A realistic workflow
The strongest results usually come from a mixed process:
- Start with a clean Figma file. Use auto layout, named layers, and consistent styles.
- Use Anima or Locofy for the first export. Choose based on speed or code structure.
- Review the code manually. Rename components, remove clutter, and fix layout issues.
- Add real logic. Connect forms, state, APIs, validation, and routing.
- Test across devices. Check mobile, tablet, desktop, keyboard use, and performance.
For a five-page marketing site, this process may save a designer and developer one to three days. For a complex SaaS dashboard, it may still save time, but only if the design file is organized and the team accepts generated code as a starting point.
Final recommendation
Choose Anima if your priority is speed, visual match, and a friendly workflow for designers. Choose Locofy if your team wants more control over components and plans to maintain the output inside a real React or Next.js codebase.
For pure screenshot-to-code work, treat every tool as an assistant, not an authority. The promise is real, but limited. These products can reduce blank-page work and speed up UI assembly. They cannot replace design systems, code review, accessibility testing, or experienced frontend decisions.
