The number of levels effect for quantitative attributes has been demonstrated to be problematic when you compare 2 levels of an attribute to 4 levels of the same attribute. Academics demonstrated that you can conduct the same conjoint analysis experiment where in one group of respondents a quantitative attribute gets 2 levels, but for a different group of respondents that same attribute is shown in 4 levels (but where levels 1 and 2 from the first group and levels 1 and 4 from the second experiment are the same). In that case, the importance of that attribute expands by about 30 to 60% when created as a 4-level attribute.
This finding got some academics some published papers in the 80s. However, a researcher from SKIM group (Marco Hoogerbrugge; https://www.sawtoothsoftware.com/download/techpap/2000Proceedings.pdf
) tested the number of levels effect in 2000 when comparing quantitative attributes with more reasonable numbers of levels, such as 3 vs. 5 levels, or 5 levels versus 9 levels, etc. He found that the number of levels effect was much reduced when comparing two experiments with more levels, such as 3 levels versus 5 levels.
Here's a sentence from his conclusions: "The conclusion is, the NOL effect clearly exists and is large when comparing 2-level attributes with more-level attributes, but the effect is questionable when you only have attributes with at least 3 levels. "
So, it seems that the number of levels effect is particularly manifest when comparing the idea of using 2 levels to a larger number of levels. But, hardly any researcher would think about representing an attribute with just 2 levels, so this seems like a corner case.
I doubt the number of levels effect would be very big when comparing a 5-level to a 7-level attribute. That said, if it's easy to do, I would try to make quantitative attributes have the same number of levels if I had the flexibility.