The Unscented Kalman Filter is a variant of the Extended Kalman Filter which uses a different linearization relying on transforming a set of "Sigma Points" instead of first-order Taylor series expansion.
The UKF does not require computing Jacobians, can be used with discontinuous transformation, and is, most importantly, more accurate than EKF for highly nonlinear transformations.
The only disadvantage I found is that "the EKF is often slightly faster than the UKF" (Probablistic Robotics). This seems negligible to me and their asymptotic complexity seems to be the same.
So why does everybody still seem to prefer EKF over UKF? Did I miss a big disadvantage of UKF?