Google and Mercor Launch Physical Engineering Benchmark; All Frontier Models Score Below 50%
Google and Mercor have introduced a new benchmark for AI agents in real-world engineering. Results show all tested frontier models score below 50%, highlighting a significant capability gap in complex physical tasks.
On November 4, 2026, the Google AI & Infrastructure team announced the launch of Physical-Engineering's Last Exam, a new benchmark designed to evaluate AI agents' ability to complete tasks in mechanical, electrical, civil, and structural engineering. Developed in collaboration with talent platform Mercor, the initiative includes the release of 29 public tasks, grading tools, and an open competition leaderboard at ple-bench.com.
Existing academic benchmarks often focus on textbook-style questions or single tool calls, failing to reflect how AI agents perform in real-world engineering environments where they must adhere to building codes, manage costs, and produce deliverables subject to licensed engineer review. Physical-Engineering's Last Exam addresses this gap with 85 long-horizon tasks built from realistic project folders containing an average of 61 files, including PDFs, spreadsheets, emails, 2D CAD drawings, and 3D BIM models. Agents must reason through these materials, identify governing standards, run calculations, and generate production-ready outputs within a containerized environment.
The Google team tested 14 frontier models using seven publicly available agent harnesses, such as Antigravity, Claude Code, and Codex. The results revealed that all models scored below 50% on the private test set, with the highest-performing model reaching only 48.3%. Furthermore, while the top configuration satisfied 91% of individual constraints, only 41.3% of its deliverables passed all hard safety and feasibility checks. These findings underscore a substantial gap between current AI capabilities and the demands of complex, high-stakes physical engineering work.