If it's a good capture, it should match exactly. The whole point is that there is no underlying model implementation. It simply does what the amp does. Input -> output, black box. That includes touch response and dynamics. Now, I'm not saying that kemper, qc, etc, will manage to reproduce this correctly all the time, but as the ML tech moves forward with GPU learning things are getting better. What you're pointing out here is an issue with training accuracy and data, not with ML based amp modeling. It's not perfect now, but then again - neither is component modeling. Take your pick and play, honestly - it doesn't matter at all anymore as long as you like it.