I think it's useful to think about timescale, which is related to, but not equivalent to, level of detail. In particular, I think it is usually better to start with a model that does not include multiple, very different, timescales.
Your hypothetical problem involving vehicle mix (strategic question, timescale = years) and seat configuration (operational question, timescale = days) is a great example of this. One would presumably want to start with a model that optimized one or the other, but not both.
But this is not a hard-and-fast rule, and it is worth some experimentation.
If the shorter-timescale decisions do not significantly affect the longer-timescale ones, they should not be included in the model. So, if the optimal vehicle mix changes significantly when you include seat configuration in the model, seat configuration should be included in the vehicle mix model. Otherwise, it should not. (Probably it will not change the mix problem, so it should not be included.)
Of course, it is always a tradeoff. As another example, facility location is a strategic problem. So it's worth asking whether we should include tactical decisions like inventory or operational decisions like routing into the facility location decision.
In the case of inventory, the inclusion of inventory changes the optimal facility locations, and moreover in at least some location–inventory models, the computational cost of adding inventory is relatively small. Therefore, it seems reasonable to include inventory in the facility location problem.
On the other hand, routing tends not to change the optimal facility locations much (I believe—someone might want to check me on that), and moreover, location–routing models are much harder to solve than straight facility location models, so the tradeoff argues for not including routing, in general.