diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
index e3ec19e93f..ee687b5334 100644
--- a/pymc/distributions/continuous.py
+++ b/pymc/distributions/continuous.py
@@ -38,7 +38,6 @@
     BetaRV,
     _gamma,
     cauchy,
-    chisquare,
     exponential,
     gumbel,
     halfcauchy,
@@ -56,7 +55,7 @@
 from pytensor.tensor.random.op import RandomVariable
 from pytensor.tensor.variable import TensorConstant
 
-from pymc.logprob.abstract import _logcdf_helper, _logprob_helper
+from pymc.logprob.abstract import _logprob_helper
 from pymc.logprob.basic import icdf
 
 try:
@@ -2374,16 +2373,21 @@ def logcdf(value, alpha, beta):
         )
 
 
-class ChiSquared(PositiveContinuous):
+class ChiSquared:
     r"""
     :math:`\chi^2` log-likelihood.
 
+    This is the distribution from the sum of the squares of :math:`\nu` independent standard normal random variables or a special
+    case of the gamma distribution with :math:`\alpha = \nu/2` and :math:`\beta = 1/2`.
+
     The pdf of this distribution is
 
     .. math::
 
        f(x \mid \nu) = \frac{x^{(\nu-2)/2}e^{-x/2}}{2^{\nu/2}\Gamma(\nu/2)}
 
+    Read more about the :math:`\chi^2` distribution at https://en.wikipedia.org/wiki/Chi-squared_distribution
+
     .. plot::
         :context: close-figs
 
@@ -2413,24 +2417,13 @@ class ChiSquared(PositiveContinuous):
     nu : tensor_like of float
         Degrees of freedom (nu > 0).
     """
-    rv_op = chisquare
-
-    @classmethod
-    def dist(cls, nu, *args, **kwargs):
-        nu = pt.as_tensor_variable(floatX(nu))
-        return super().dist([nu], *args, **kwargs)
 
-    def moment(rv, size, nu):
-        moment = nu
-        if not rv_size_is_none(size):
-            moment = pt.full(size, moment)
-        return moment
+    def __new__(cls, name, nu, **kwargs):
+        return Gamma(name, alpha=nu / 2, beta=1 / 2, **kwargs)
 
-    def logp(value, nu):
-        return _logprob_helper(Gamma.dist(alpha=nu / 2, beta=0.5), value)
-
-    def logcdf(value, nu):
-        return _logcdf_helper(Gamma.dist(alpha=nu / 2, beta=0.5), value)
+    @classmethod
+    def dist(cls, nu, **kwargs):
+        return Gamma.dist(alpha=nu / 2, beta=1 / 2, **kwargs)
 
 
 # TODO: Remove this once logp for multiplication is working!
diff --git a/tests/distributions/test_continuous.py b/tests/distributions/test_continuous.py
index 3d74b00bff..9bb147e7c9 100644
--- a/tests/distributions/test_continuous.py
+++ b/tests/distributions/test_continuous.py
@@ -1132,19 +1132,6 @@ def test_beta_moment(self, alpha, beta, size, expected):
             pm.Beta("x", alpha=alpha, beta=beta, size=size)
         assert_moment_is_expected(model, expected)
 
-    @pytest.mark.parametrize(
-        "nu, size, expected",
-        [
-            (1, None, 1),
-            (1, 5, np.full(5, 1)),
-            (np.arange(1, 6), None, np.arange(1, 6)),
-        ],
-    )
-    def test_chisquared_moment(self, nu, size, expected):
-        with pm.Model() as model:
-            pm.ChiSquared("x", nu=nu, size=size)
-        assert_moment_is_expected(model, expected)
-
     @pytest.mark.parametrize(
         "lam, size, expected",
         [
@@ -2243,19 +2230,6 @@ class TestInverseGammaMuSigma(BaseTestDistributionRandom):
     checks_to_run = ["check_pymc_params_match_rv_op"]
 
 
-class TestChiSquared(BaseTestDistributionRandom):
-    pymc_dist = pm.ChiSquared
-    pymc_dist_params = {"nu": 2.0}
-    expected_rv_op_params = {"nu": 2.0}
-    reference_dist_params = {"df": 2.0}
-    reference_dist = seeded_numpy_distribution_builder("chisquare")
-    checks_to_run = [
-        "check_pymc_params_match_rv_op",
-        "check_pymc_draws_match_reference",
-        "check_rv_size",
-    ]
-
-
 class TestLogistic(BaseTestDistributionRandom):
     pymc_dist = pm.Logistic
     pymc_dist_params = {"mu": 1.0, "s": 2.0}
diff --git a/tests/logprob/test_transform_value.py b/tests/logprob/test_transform_value.py
index 497231cd6b..7f88099cec 100644
--- a/tests/logprob/test_transform_value.py
+++ b/tests/logprob/test_transform_value.py
@@ -26,6 +26,8 @@
 from pytensor.graph import FunctionGraph
 from pytensor.graph.basic import equal_computations
 
+import pymc as pm
+
 from pymc.distributions.transforms import _default_transform, log, logodds
 from pymc.logprob import conditional_logp
 from pymc.logprob.abstract import MeasurableVariable, _logprob
@@ -154,7 +156,7 @@ def test_original_values_output_dict():
             (),
         ),
         (
-            pt.random.chisquare,
+            pm.ChiSquared.dist,
             (1.5,),
             lambda df: sp.stats.chi2(df),
             (),
diff --git a/tests/logprob/test_transforms.py b/tests/logprob/test_transforms.py
index 9acd3ab1ce..4e96412c78 100644
--- a/tests/logprob/test_transforms.py
+++ b/tests/logprob/test_transforms.py
@@ -43,7 +43,7 @@
 
 from pytensor.graph.basic import equal_computations
 
-from pymc.distributions.continuous import Cauchy
+from pymc.distributions.continuous import Cauchy, ChiSquared
 from pymc.logprob.basic import conditional_logp, icdf, logcdf, logp
 from pymc.logprob.transforms import (
     ArccoshTransform,
@@ -431,7 +431,7 @@ def test_sqr_transform(self):
 
     def test_sqrt_transform(self):
         # The sqrt of a chisquare with n df is a chi distribution with n df
-        x_rv = pt.sqrt(pt.random.chisquare(df=3, size=(4,)))
+        x_rv = pt.sqrt(ChiSquared.dist(nu=3, size=(4,)))
         x_rv.name = "x"
 
         x_vv = x_rv.clone()