Due to my newness to the field, I can only talk about comparisons of biological plausibility when discussing the Neural Engineering Framework (NEF) and functional modeling. What is missing from this answer is a purely bottom-up modelling perspective in the same vein as the Blue-Brain project, but I'll leave that to another user.
One of the claims driving the push towards cognitive modelers using the NEF is the fact that it is more biologically plausible than back-prop ANNs. What is typically discussed is the neural mechanism for encoding, decoding and learning of the NEF has more scientific evidence claiming their plausibility than back-prop ANNs.
From a more systematic biologically plausible perspective, the NEF is also used to build neural networks that use Semantic Pointer Architecture, which is also claimed to be more biologically plausible for a number of reasons. First of all, the number of neurons required for most models in significantly less than other proposed models (see Eric Crawford's work on knowledge representation). Secondly, modifications to the network mimicking the effects of neurological disorders can be shown to have the same effect of those disorders (see Dan Rasmussen's work on general intelligence).
In terms of the bottom up perspective, the NEF could help here as well. The NEF is neuron-model agnostic, so it can be used with any of the neuron models you mentioned in the question. Hypothetically, it could be possible to create a similar network to one grown on a Neurochip and compare the various inputs and outputs as you suggested, but I've never heard of such a study being done.