diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index ef81c68281..70c50502f7 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -3388,6 +3388,12 @@ def dist(cls, mu=0.0, s=1.0, *args, **kwargs):
         s = at.as_tensor_variable(floatX(s))
         return super().dist([mu, s], *args, **kwargs)
 
+    def get_moment(rv, size, mu, s):
+        mu, _ = at.broadcast_arrays(mu, s)
+        if not rv_size_is_none(size):
+            mu = at.full(size, mu)
+        return mu
+
     def logcdf(value, mu, s):
         r"""
         Compute the log of the cumulative distribution function for Logistic distribution
diff --git a/pymc/tests/test_distributions_moments.py b/pymc/tests/test_distributions_moments.py
index 591b8decdf..9e2d56afdb 100644
--- a/pymc/tests/test_distributions_moments.py
+++ b/pymc/tests/test_distributions_moments.py
@@ -17,6 +17,7 @@
     HalfStudentT,
     Kumaraswamy,
     Laplace,
+    Logistic,
     LogNormal,
     Poisson,
     StudentT,
@@ -413,3 +414,23 @@ def test_constant_moment(c, size, expected):
     with Model() as model:
         Constant("x", c=c, size=size)
     assert_moment_is_expected(model, expected)
+
+
+@pytest.mark.parametrize(
+    "mu, s, size, expected",
+    [
+        (1, 1, None, 1),
+        (1, 1, 5, np.full(5, 1)),
+        (2, np.arange(1, 6), None, np.full(5, 2)),
+        (
+            np.arange(1, 6),
+            np.arange(1, 6),
+            (2, 5),
+            np.full((2, 5), np.arange(1, 6)),
+        ),
+    ],
+)
+def test_logistic_moment(mu, s, size, expected):
+    with Model() as model:
+        Logistic("x", mu=mu, s=s, size=size)
+    assert_moment_is_expected(model, expected)