diff --git a/pymc/tests/test_distributions_random.py b/pymc/tests/test_distributions_random.py
index a04362a6ba2..eedf8301681 100644
--- a/pymc/tests/test_distributions_random.py
+++ b/pymc/tests/test_distributions_random.py
@@ -1698,33 +1698,33 @@ class TestOrderedProbit(BaseTestDistributionRandom):
         "check_rv_size",
     ]
 
-    def test_vector_inputs(self):
+    @pytest.mark.parametrize(
+        "eta, cutpoints, sigma, expected",
+        [
+            (0, [-2.0, 0, 2.0], 1.0, (4,)),
+            ([-1], [-2.0, 0, 2.0], [2.0], (1, 4)),
+            ([1.0, -2.0], [-1.0, 0, 1.0], 1.0, (2, 4)),
+            ([1.0, -2.0, 3.0], [-2.0, 0, 2.0], [-1.0, -2.0, 5.0], (3, 4)),
+            ([[1.0, -1.0, 0.0], [-1.0, 3.0, 5.0]], [-2.0, 0, 1.0], [-1.0, -2.0, 5.0], (2, 3, 4)),
+            (
+                [[1.0, -2.0, 3.0], [1.0, 2.0, -4.0]],
+                [-2.0, 0, 1.0],
+                [[0.0, 2.0, -4.0], [-1.0, 1.0, 3.0]],
+                (2, 3, 4),
+            ),
+        ],
+    )
+    def test_shape_inputs(self, eta, cutpoints, sigma, expected):
         """
-        This test checks when providing vector inputs for `eta` and `sigma` parameters using advanced indexing.
+        This test checks when providing different shapes for `eta` and `sigma` parameters.
         """
-        categorical = pm.OrderedProbit.dist(
-            eta=0,
-            cutpoints=np.array([-2.0, 0, 2.0]),
-            sigma=1.0,
-        )
-        p = categorical.owner.inputs[3].eval()
-        assert p.shape == (4,)
-
-        categorical = pm.OrderedProbit.dist(
-            eta=np.array([1.0, 2.0, 3.0, 4.0, 5.0]),
-            cutpoints=np.array([-2.0, 0, 2.0]),
-            sigma=1,
-        )
-        p = categorical.owner.inputs[3].eval()
-        assert p.shape == (5, 4)
-
-        categorical = pm.OrderedProbit.dist(
-            eta=np.array([1.0, 2.0, 3.0, 4.0, 5.0]),
-            cutpoints=np.array([-2.0, 0, 2.0]),
-            sigma=np.array([1.0, 2.0, 3.0, 4.0, 5.0]),
+        categorical = _OrderedProbit.dist(
+            eta=eta,
+            cutpoints=cutpoints,
+            sigma=sigma,
         )
         p = categorical.owner.inputs[3].eval()
-        assert p.shape == (5, 4)
+        assert p.shape == expected
 
 
 class TestOrderedMultinomial(BaseTestDistributionRandom):