In any setting in which observable properties have a quantitative flavor, it is natural to compare computational objects by way of metrics rather than equivalences or partial orders. This holds, in particular , for probabilistic higher-order programs. A natural notion of comparison , then, becomes context distance, the metric analogue of Morris' context equivalence. In this paper, we analyze the main properties of the context distance in fully-fledged probabilistic λ-calculi, this way going beyond the state of the art, in which only affine calculi were considered. We first of all study to which extent the context distance trivializes, giving a sufficient condition for trivialization. We then characterize context distance by way of a coinductively-defined, tuple-based notion of distance in one of those calculi, called Λ ⊕ !. We finally derive pseudomet-rics for call-by-name and call-by-value probabilistic λ-calculi, and prove them fully-abstract.