People seem to be comparing it to Simulink, but to me the language seems to have more in common with matlab / octave's symbolic package / python's sympy.
The difference you are pointing to is the acausal vs causal description. In Simulink (Causal), you have to describe what causes any changes in the system. For example, to model Ohm's law, you'd write something like I=V/R. Which translates to the current obtained when some potential difference is applied across a resistor. So I is a function of V in this respect and you can say V causes I in R.
In Acausal systems like modelica, it does not matter whether you write V=IR or any alternate formulation. You simply provide the description and it is up to the compiler to obtain the relevant set of equations that can be fed into the ODE solver. The advantage being that you spend less time fitting your problem into the causal framework required by Simulink or alternates where, when you change your model, you end up doing significant rewrites.
TL;DR: Causal descriptions take more time to arrange as compared to Acausal.
"In which case, isn't comparing it to Simulink instead of Sympy a bit ... odd?"
No, as it is not used as Sympy. Modelica is used to model physical systems in a composable way with graphical representation of connections between components. Many people use Simulink for that, so this is why it is compared with it.
Sympy is a nice project, but is applied in a different context, definitely not to model systems with thousands of equations in the industrial setting (that would be painful).
Any idea if that's the case?