ModelRift recently evaluated two code-first CAD tools, OpenSCAD and CadQuery, to determine which better supports autonomous AI generation of functional 3D-printable parts. The focus was on how effectively an AI agent could produce correct models without human intervention, rather than on manual coding experience.
The experiment involved six AI agents running three design tasks using each tool. The AI was powered by Claude Opus 5 and operated in isolated environments to avoid cross-contamination. Both CAD tools were tested on an M-series Mac with CadQuery 2.8.0 on Python 3.14 and OpenSCAD 2026.06.12. ModelRift adapted their internal OpenSCAD skill for CadQuery, including a custom offscreen renderer and B-rep validity checks to match OpenSCAD’s capabilities.
The tasks ranged from a simple L-bracket shelf support, to a two-part snap-fit enclosure for a PCB, and finally a complex M24x2 threaded hose-barb adapter requiring true helical geometry without stacked rings.
Both tools successfully produced printable models for all tasks. However, the nature of their failures differed. CadQuery tended to fail loudly and early with exceptions, forcing the AI to address errors immediately. OpenSCAD, in contrast, often failed silently, producing flawed geometry without error messages, which poses risks for unattended generation.
Verification emerged as a key differentiator. CadQuery’s B-rep kernel allows querying geometry properties and embedding assertions directly into the model, enabling automated checks for dimensions and interferences. OpenSCAD lacks this introspection, requiring external STL parsing to validate outputs.
Performance-wise, OpenSCAD was significantly faster in geometry recomputation (milliseconds versus seconds), though this difference was minor compared to AI inference times.
Visual rendering was found insufficient for detecting subtle errors. Instead, numeric checks—such as volume measurements, interference tests, and mesh audits—were essential for reliable validation. Both tools’ internal validity reports were sometimes misleading, underscoring the need for independent mesh inspection.
ModelRift concluded that neither tool outperformed the other in output quality, but CadQuery’s verification capabilities provide a substantial advantage for autonomous workflows. Despite this, ModelRift will continue using OpenSCAD due to its compact syntax, sandboxed environment, faster rendering, and existing infrastructure.
The study highlights that effective AI-driven CAD generation depends more on robust verification and numeric feedback than on rendering quality alone. ModelRift plans to enhance their OpenSCAD skill with improved edge rendering and mandatory numeric checks to improve autonomous model validation.
This benchmark provides valuable insights for developers and researchers working on AI-assisted CAD, emphasizing the importance of error transparency and automated verification in generating reliable 3D-printable designs.