---
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](https://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

  ```python
  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