Theoretically this is precisely true, but in practice the difference is negligible for many models given the incredible computer power of modern hardware. A particle filter with a million points is trivial to model on even a mobile phone GPU with milliwatt levels of power draw.
Even "simple" cases like blending GPS data with other sensors could potentially benefit from a particle filter model. For example, an advanced particle filter could model the GPS signal echoing off buildings, which might result in multiple local maxima in the location probability space.
Most (all?) current systems simply throw away the other peaks in the GPS signal and feed in only the one with the "highest likelyhood".
Similarly, wheel tick counters typically use one wheel as an input. A particle filter could take all four wheels and be able to model disagreements in a robust way.
Particle filters are also better where the probabilities are not Gaussian. E.g.: measurements that are strictly positive often don't have Gaussian PDFs.