Not because the data is hard to find, but because their makers have never published it. This is not a gap in our figures so much as a gap in theirs.
Energy per answer is driven by how much of a model fires for each word it writes. Without a parameter count, published by the maker or derivable from what they have said, there is nothing to calculate from. No AI company publishes per-answer energy directly, so a size is the only route anyone outside has.
8 models here publish no size at any source. A further set publish so little that the plausible answers sit about 30× apart, those are shown as wide brackets and barred from every headline claim on this site, rather than being quietly guessed at.
Makers with fewer than 3 tracked models are left out, a percentage of one or two says more about the sample than the company.
Every other industry that consumes energy at scale is expected to say how much. A power rating on an appliance, a fuel figure on a car, a rating on a building. AI has grown to a scale where its consumption is a public question, and the basic number needed to answer it is treated as commercially sensitive.
We could have closed the gap by guessing. Assuming one architecture over another moves the answer roughly thirty-fold, so a guess would have produced a number that looked precise and meant nothing. Leaving the gap visible is the more honest result, and it is the finding, not a footnote to it.