Deep learning–based fractional flow reserve derived from coronary CT angiography (CT-FFR) enables noninvasive assessment of lesion-specific ischemia. Onsite CT-FFR systems provide near–real-time physiologic evaluation at the workstation, potentially reducing unnecessary invasive testing. This study evaluated the diagnostic performance of a novel onsite deep learning CT-FFR algorithm compared with invasive instantaneous wave-free ratio (iFR).