diff --git a/tests/regression_tests/filter_energyfun/inputs_true.dat b/tests/regression_tests/filter_energyfun/inputs_true.dat
index 8c3db35186..133b3167f2 100644
--- a/tests/regression_tests/filter_energyfun/inputs_true.dat
+++ b/tests/regression_tests/filter_energyfun/inputs_true.dat
@@ -39,6 +39,21 @@
     <y>0.1 0.1 0.1333 0.158 0.18467 0.25618 0.4297 0.48 0.48</y>
     <interpolation>log-linear</interpolation>
   </filter>
+  <filter id="6" type="energyfunction">
+    <energy>0.0 5000000.0 10000000.0 15000000.0</energy>
+    <y>0.2 0.7 0.7 0.2</y>
+    <interpolation>linear-linear</interpolation>
+  </filter>
+  <filter id="7" type="energyfunction">
+    <energy>0.0 5000000.0 10000000.0 15000000.0</energy>
+    <y>0.2 0.7 0.7 0.2</y>
+    <interpolation>quadratic</interpolation>
+  </filter>
+  <filter id="8" type="energyfunction">
+    <energy>0.0 5000000.0 10000000.0 15000000.0</energy>
+    <y>0.2 0.7 0.7 0.2</y>
+    <interpolation>cubic</interpolation>
+  </filter>
   <tally id="1">
     <nuclides>Am241</nuclides>
     <scores>(n,gamma)</scores>
@@ -63,4 +78,19 @@
     <nuclides>Am241</nuclides>
     <scores>(n,gamma)</scores>
   </tally>
+  <tally id="6">
+    <filters>6</filters>
+    <nuclides>Am241</nuclides>
+    <scores>(n,gamma)</scores>
+  </tally>
+  <tally id="7">
+    <filters>7</filters>
+    <nuclides>Am241</nuclides>
+    <scores>(n,gamma)</scores>
+  </tally>
+  <tally id="8">
+    <filters>8</filters>
+    <nuclides>Am241</nuclides>
+    <scores>(n,gamma)</scores>
+  </tally>
 </tallies>
diff --git a/tests/regression_tests/filter_energyfun/results_true.dat b/tests/regression_tests/filter_energyfun/results_true.dat
index c0b59680e8..0aadac82e9 100644
--- a/tests/regression_tests/filter_energyfun/results_true.dat
+++ b/tests/regression_tests/filter_energyfun/results_true.dat
@@ -6,3 +6,5 @@
 0  b4e2ac84068d2d   Am241  (n,gamma) 8.19e-02   2.25e-03
    energyfunction nuclide      score     mean  std. dev.
 0  dacf88242512ea   Am241  (n,gamma) 7.95e-02   2.19e-03
+   energyfunction nuclide      score     mean  std. dev.
+0  fe168c70d9e078   Am241  (n,gamma) 1.06e-01   2.96e-03
diff --git a/tests/regression_tests/filter_energyfun/test.py b/tests/regression_tests/filter_energyfun/test.py
index 9c2be7be39..12144c1fda 100644
--- a/tests/regression_tests/filter_energyfun/test.py
+++ b/tests/regression_tests/filter_energyfun/test.py
@@ -48,9 +48,21 @@ def model():
     filt5 = openmc.EnergyFunctionFilter(x, y)
     filt5.interpolation = 'log-linear'
 
-    filters = [filt1, filt3, filt4, filt5]
+    # define a trapezoidal function for comparison
+    x = [0.0, 5e6, 1e7, 1.5e7]
+    y = [0.2, 0.7, 0.7, 0.2]
+
+    filt6 = openmc.EnergyFunctionFilter(x, y)
+
+    filt7 = openmc.EnergyFunctionFilter(x, y)
+    filt7.interpolation = 'quadratic'
+
+    filt8 = openmc.EnergyFunctionFilter(x, y)
+    filt8.interpolation = 'cubic'
+
+    filters = [filt1, filt3, filt4, filt5, filt6, filt7, filt8]
     # Make tallies
-    tallies = [openmc.Tally() for i in range(5)]
+    tallies = [openmc.Tally() for _ in range(len(filters) + 1)]
     for t in tallies:
         t.scores = ['(n,gamma)']
         t.nuclides = ['Am241']
@@ -73,7 +85,7 @@ class FilterEnergyFunHarness(PyAPITestHarness):
         br_tally = sp.tallies[2] / sp.tallies[1]
         dataframes_string += br_tally.get_pandas_dataframe().to_string() + '\n'
 
-        for t_id in (3, 4, 5):
+        for t_id in (3, 4, 5, 6):
             ef_tally = sp.tallies[t_id]
             dataframes_string += ef_tally.get_pandas_dataframe().to_string() + '\n'
 
@@ -122,7 +134,16 @@ class FilterEnergyFunHarness(PyAPITestHarness):
         sp_log_lin_filt = sp_log_lin_tally.find_filter(openmc.EnergyFunctionFilter)
         assert sp_log_lin_filt.interpolation == 'log-linear'
 
+        # check that the cubic interpolation provides a higher value
+        # than linear-linear
+        contrived_lin_lin_tally = sp.get_tally(id=6)
+        contrived_quadratic_tally = sp.get_tally(id=7)
+        contrived_cubic_tally = sp.get_tally(id=8)
+
+        assert all(contrived_lin_lin_tally.mean < contrived_quadratic_tally.mean)
+        assert all(contrived_lin_lin_tally.mean < contrived_cubic_tally.mean)
+
 
 def test_filter_energyfun(model):
     harness = FilterEnergyFunHarness('statepoint.5.h5', model)
-    harness.main()
+    harness.main()
\ No newline at end of file