Text2CAD-Bench 🏭

Text2CAD-Bench is the first comprehensive benchmark for evaluating text-to-CAD generation across geometric complexity and application diversity.

📢 News

• [2026.02] 🎉 v1.0 released with 30% prompts for preview
• [Coming Soon] v1.1 will include additional evaluation scripts and expanded documentation

📖 Overview

Text2CAD-Bench comprises 600 human-curated examples organized into four benchmark levels:

Level	Description	Examples	Key Features
L1	Basic	200	Primitives, simple spatial relationships
L2	Intermediate	200	Boolean operations, chamfer, fillet, patterns
L3	Advanced	100	Sweep, loft, shell, complex surfaces
L4	Real-world	100	Multi-domain applications

Each example includes dual-style prompts:

• Geometric (Geo): Appearance-based descriptions mimicking non-expert users
• Sequence (Seq): Procedural descriptions aligned with expert-level CAD conventions

📁 Dataset Structure

Text2CAD-Bench/
├── prompts/                    # 30% sample prompts (preview)
│   ├── L1/
│   │   ├── L1_001_geo
│   │   ├── L1_001_seq
│   │   └── ...
│   ├── L2/
│   ├── L3/
│   └── L4/
├── evaluation/                 # Evaluation scripts
│   ├── metrics.py
│   ├── evaluate.py
│   └── requirements.txt
├── examples/                   # Example outputs
│   └── visualizations/
└── README.md

⚠️ Note: Ground truth STEP files are not publicly released to prevent benchmark contamination. The 30% prompt samples are provided to demonstrate data distribution and format. For full benchmark access, please contact us.

🏆 Leaderboard

📊 Interactive Leaderboard: See leaderboard for sortable results by different metrics.

Final results are weighted by sample count: L1 (200, 40%), L2 (200, 40%), L3 (100, 20%).

General-purpose LLMs (Sorted by CD ↓)

Rank	Model	CD ↓	IR ↓	IoU ↑
🥇	GPT-5.2	63.97	30.6%	0.45
🥈	Claude-4.5-Sonnet	66.90	41.3%	0.43
🥉	DeepSeek-V3.2	76.25	29.7%	0.37
4	MiniMax M2.11	83.16	42.7%	0.37
5	GLM-4.7	84.98	35.0%	0.34
6	Qwen3-max	99.21	43.2%	0.28

Domain-specific Models (Sorted by CD ↓)

Rank	Model	CD ↓	IR ↓	IoU ↑
🥇	CADFusion	224.35	60.5%	0.03
🥈	Text2CAD	248.66	7.0%	0.05
🥉	Text2CADQuery	250.27	51.0%	0.04

🚀 Quick Start

Installation

git clone https://github.com/xxx/Text2CAD-Bench.git
cd Text2CAD-Bench
pip install -r evaluation/requirements.txt

Evaluation

from evaluation import evaluate

# Load your model outputs
results = evaluate(
    predictions_dir="path/to/your/outputs",
    metrics=["CD", "IR", "IoU"]
)

print(results.summary())

Submit to Leaderboard

To submit your results to the leaderboard:

1. Run evaluation on the full benchmark by upload your model.
2. Generate results file using our evaluation script
3. Submit via Google Form or email

python evaluation/generate_submission.py \
    --predictions_dir path/to/outputs \
    --output submission.json

📜 License

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

You are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

• Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.

📧 Contact

• Email:
• Issues: Please use GitHub Issues for bug reports and feature requests
• Full benchmark access: Contact us with your affiliation and intended use

🙏 Acknowledgements

We thank all annotators and reviewers who contributed to the construction of Text2CAD-Bench.

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Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation