BIM-Edit: Benchmarking Large Language Models for IFC-Based Building Information Modeling
For researchers and practitioners in CAD/BIM, this benchmark highlights the inadequacy of current LLMs for structured editing tasks involving geometry, semantics, and topology.
BIM-Edit benchmarks LLMs on editing Building Information Models (BIM) from natural language, finding the best model achieves only 49.5% average score and no model fully solves more than 3.4% of tasks, revealing a large gap between current LLM capabilities and engineering needs.
Large language models (LLMs) are increasingly applied to computer-aided design (CAD) to generate design artifacts from textual instructions. In engineering practice, this requires more than creating new geometry, models must also understand existing scenes, edit them correctly, and preserve semantics and relations. However, many CAD benchmarks focus on creating new models rather than editing existing ones, and mostly evaluate geometric correctness. We introduce BIM-Edit, a benchmark for evaluating LLMs on natural-language editing of Building Information Models (BIM) represented in the Industry Foundation Classes (IFC) format. BIM provides a challenging testbed because building models encode geometry together with semantic and relational structure. BIM-Edit contains 324 editing tasks spanning 11 realistic building models and 36 synthetic scenes. Tasks are expressed using three instruction categories - direct, spatial, and topological - covering both explicit and scene-grounded edits. We evaluate outputs along three dimensions: geometric accuracy, semantic validity, and topological consistency. Across evaluated LLMs, the best-performing model achieves only 49.5% average score across the three metrics, and no model fully solves more than 3.4% of tasks. These results demonstrate a substantial gap between current LLM capabilities and the requirements of structured engineering design workflows.