Xpeng has launched an embodied perception foundation model named GroundingPI, reporting an overall benchmark score that surpassed OpenAI’s frontier model GPT-6 Astra across 34 public perception tests. Built on a parameter scale of about 4 billion, the perception-first architecture ranked first in comprehensive evaluations and demonstrated higher data processing efficiency in robot simulation tasks. Xpeng developed the model in partnership with research teams from Peking University and the University of Hong Kong, aiming to significantly reduce the cost of manually collecting robot demonstration data. In a direct comparison across 11 capability categories, GroundingPI recorded a score of 73.68, edging out the 71.54 achieved by GPT-6 Astra, which OpenAI released in September this year. The code and model weights have been open-sourced. Xpeng said existing general-purpose large models are primarily designed for digital scenarios and face an adaptation gap when applied directly to physical scenarios such as autonomous driving and robotics. GroundingPI does not use robot data with joint action labels during the training phase, relying entirely on pure perception data without action labels to cover 11 capability categories including general detection, text recognition, and robot spatial localization. Xpeng said GroundingPI aims to provide a perception-native foundation for the fast system rather than replacing the general large model capabilities of the slow system. The research is currently at the scientific prototype stage, but its simulation results have provided validation for 2 core challenges in scaled deployment.