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frontend-figma-to-code

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metadata 
task_type: figma_to_code
dataset_info:
  features:
    - name: id
      dtype: string
    - name: url
      dtype: string
    - name: tags
      list: string
    - name: description
      dtype: string
    - name: figma_data
      dtype: string
    - name: access_level
      dtype: string
    - name: created_at
      dtype: string
  splits:
    - name: train
      num_bytes: 64990
      num_examples: 37
  download_size: 35843
  dataset_size: 64990
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
license: mit
language:
  - en
tags:
  - figma-to-code
  - autonomous-agents
pretty_name: CREW:Figma-to-Code
size_categories:
  - n<1K

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