Impressive, but there's an easy formula for getting these systems to make mistakes. Just input a sentence with some kind of long distance dependency. For example, DeepL gets agreement right in English to Spanish translations when the two things that agree are close together:

    I like soup -> COMO sopa
    They eat soup -> COMEN sopa

Impressively, it can even get agreement correct across clause boundaries in many cases. But if you do wh-movement through two or more clauses, you're usually out of luck:

    Which boys does he say he believes eat soup?
    ->
    ¿Qué chicos dice que cree que COME sopa? [should be COMEN]

It doesn't really matter very much in practice if an MT system makes mistakes like this, but they are mistakes that you can rely on humans not to make systematically.