diff --git a/src/storage/model.cpp b/src/storage/model.cpp
index 27baa1d..510c936 100644
--- a/src/storage/model.cpp
+++ b/src/storage/model.cpp
@@ -119,6 +119,7 @@ void define_model(py::module& m) {
         .def_property_readonly("reward_models", [](Model<double>& model) {return model.getRewardModels(); }, "Reward models")
         .def_property_readonly("transition_matrix", &getTransitionMatrix<double>, py::return_value_policy::reference, py::keep_alive<1, 0>(), "Transition matrix")
         .def_property_readonly("backward_transition_matrix", &getBackwardTransitionMatrix<double>, py::return_value_policy::reference, py::keep_alive<1, 0>(), "Backward transition matrix")
+        .def("reduce_to_state_based_rewards", &Model<double>::reduceToStateBasedRewards)
         .def("__str__", getModelInfoPrinter<double>())
     ;
     py::class_<Dtmc<double>, std::shared_ptr<Dtmc<double>>>(m, "SparseDtmc", "DTMC in sparse representation", model)
@@ -138,6 +139,7 @@ void define_model(py::module& m) {
         .def_property_readonly("transition_rewards", [](RewardModel<double>& rewardModel) {return rewardModel.getTransitionRewardMatrix();})
         .def_property_readonly("state_rewards", [](RewardModel<double>& rewardModel) {return rewardModel.getStateRewardVector();})
         .def_property_readonly("state_action_rewards", [](RewardModel<double>& rewardModel) {return rewardModel.getStateActionRewardVector();})
+        .def("reduce_to_state_based_rewards", [](RewardModel<double>& rewardModel, SparseMatrix<double> const& transitions, bool onlyStateRewards){return rewardModel.reduceToStateBasedRewards(transitions, onlyStateRewards);},  py::arg("transition_matrix"), py::arg("only_state_rewards"), "Reduce to state-based rewards")
             ;
 
 
diff --git a/tests/storage/test_model.py b/tests/storage/test_model.py
index dec72ba..9f25182 100644
--- a/tests/storage/test_model.py
+++ b/tests/storage/test_model.py
@@ -43,6 +43,20 @@ class TestModel:
         assert not model.supports_parameters
         assert type(model) is stormpy.SparseDtmc
 
+    def test_reduce_to_state_based_rewards(self):
+        program = stormpy.parse_prism_program(get_example_path("dtmc", "die.pm"))
+        prop = "R=? [F \"done\"]"
+        properties = stormpy.parse_properties_for_prism_program(prop, program, None)
+        model = stormpy.build_model(program, properties)
+        model.reduce_to_state_based_rewards()
+        assert len(model.reward_models) == 1
+        assert model.reward_models["coin_flips"].has_state_rewards
+        assert not model.reward_models["coin_flips"].has_state_action_rewards
+        for reward in model.reward_models["coin_flips"].state_rewards:
+            assert reward == 1.0 or reward == 0.0
+        assert not model.reward_models["coin_flips"].has_transition_rewards
+
+
     def test_build_parametric_dtmc_from_prism_program(self):
         program = stormpy.parse_prism_program(get_example_path("pdtmc", "brp16_2.pm"))
         prop = "P=? [F s=5]"