metaphilabs/frontend-figma-to-code

CREW: Figma to Code

A benchmark for evaluating AI coding agents on Figma-to-code generation — converting real-world Figma community designs into production-ready React + Tailwind CSS applications.

Each task gives an agent full access to a Figma file via MCP tools. The agent must extract the design system, generate components, build successfully, and deploy a live preview. Outputs are evaluated through human preference (ELO) and automated verifiers.

Benchmark at a Glance

Tasks	37 real Figma community designs
Agents tested	Claude Code (Opus 4.6), Codex (GPT-5.2), Gemini CLI (3.1 Pro)
Total runs	96 autonomous agent executions
Human evaluations	135 pairwise preference votes
Primary metric	Human Preference ELO (Bradley-Terry)
Task duration	6–40 expert-hours equivalent per task

Leaderboard (Human Preference ELO)

Rank	Agent	Model	ELO	95% CI	Win %
1	Codex	GPT-5.2	1054	[1005, 1114]	56.8%
2	Claude Code	Opus 4.6	1039	[987, 1093]	55.1%
3	Gemini CLI	3.1 Pro	907	[842, 965]	31.1%

Top two agents are statistically indistinguishable (p=0.67, Cohen's h=0.08); both significantly outperform Gemini CLI (p<0.05).

Live leaderboard: evals.metaphi.ai

Dataset Schema

Each row represents one Figma design task:

Column	Type	Description
id	string	Unique task identifier
figma_data.figma_file_key	string	Figma file ID for API access
figma_data.figma_file_url	string	Full Figma URL
figma_data.figma_file_name	string	Design name/title
figma_data.is_site	bool	Whether design is a Figma Site
description	string	Design context and task description

Task Scenarios

Tasks span 7 complexity levels, from single-component extraction to full multi-page applications:

1. E-commerce Product Page (12 hrs) — PDP with image gallery, variant selectors, inventory states, cart integration
2. Mobile Onboarding Flow (16 hrs) — Multi-step flow with transitions, conditional branching, state management
3. Component Set with States (6 hrs) — Variant matrix extraction, typed props, conditional rendering
4. Design Tokens to Theme (8 hrs) — Variables, typography, effects → Tailwind config + CSS custom properties
5. Multi-Page Webapp (40 hrs) — 5+ pages with routing, shared components, consistent theming
6. Animation-Heavy Interface (20 hrs) — Smart Animate → Framer Motion with precise timing choreography
7. Messy Enterprise File (24 hrs) — Real-world chaos: inconsistent naming, duplicates, orphaned components

Data Curation

Sourced from licensing partnerships with enterprises, domain-experts and community dataset curation.

Evaluation Framework

Verifier	Type	Method
Human Preference	Subjective	Pairwise comparison → Bradley-Terry ELO
Visual Judge	Subjective	VLM screenshot comparison (design vs. output)
Skill Verifier	Subjective	Task-specific rubrics (build, tokens, components, fidelity)
Behavior Verifier	Subjective	Agent trajectory analysis (error recovery, tool usage)

Agent Error Recovery

Across 96 runs, agents encountered 590 errors with a 70.3% autonomous recovery rate:

Error Type	Count	Recovery
Tool call failure	419	66.3%
Git error	64	75.0%
Syntax error	33	90.9%
Build error	11	100.0%

Usage

from datasets import load_dataset

ds = load_dataset("metaphilabs/figma", split="train")

# Each row contains a Figma file key for API access
for task in ds:
    print(task["id"], task["figma_data"]["figma_file_name"])

Citation

@misc{metaphi2026crew,
  title={CREW: Enterprise Agent Benchmarks},
  author={Metaphi Labs},
  year={2026},
  url={https://evals.metaphi.ai}
}

Links

- Leaderboard: https://evals.metaphi.ai
- Website: https://metaphi.ai
- Collection: https://huggingface.co/collections/metaphilabs/crew