diff --git a/doc/source/getting_started.rst b/doc/source/getting_started.rst
index c38afd7..01598ff 100644
--- a/doc/source/getting_started.rst
+++ b/doc/source/getting_started.rst
@@ -169,7 +169,13 @@ One powerful part of the storm model checker is to quickly create the Markov cha
 	>>> prism_program = stormpy.parse_prism_program(path)
     >>> model = stormpy.build_model(prism_program)
 
-In this example, we will exploit this, and explore the underlying matrix of the model.
+In this example, we will exploit this, and explore the underlying Markov chain of the model.
+The most basic question might be what the type of the constructed model is::
+
+    >>> print(model.model_type)
+    ModelType.DTMC
+
+We can also directly explore the underlying matrix.
 Notice that this code can be applied to both deterministic and non-deterministic models::
 
     >>> for state in model.states:
diff --git a/examples/06-getting-started.py b/examples/06-getting-started.py
index 66481bb..d656d7a 100644
--- a/examples/06-getting-started.py
+++ b/examples/06-getting-started.py
@@ -12,6 +12,8 @@ def example_getting_started_06():
     properties = stormpy.parse_properties_for_prism_program(formula_str, prism_program)
     model = stormpy.build_model(prism_program, properties)
 
+    print(model.model_type)
+
     for state in model.states:
         for action in state.actions:
             for transition in action.transitions: