diff --git a/coremltools/converters/mil/mil/ops/defs/iOS15/activation.py b/coremltools/converters/mil/mil/ops/defs/iOS15/activation.py
index 0c261a1f2..aede13d7d 100644
--- a/coremltools/converters/mil/mil/ops/defs/iOS15/activation.py
+++ b/coremltools/converters/mil/mil/ops/defs/iOS15/activation.py
@@ -77,9 +77,14 @@ class clamped_relu(activation_with_alpha_and_beta):
 
     @precondition(allow=VALUE)
     def value_inference(self):
-        x = np.minimum(np.maximum(self.x.val, 0), self.beta.val)
-        y = np.minimum(np.minimum(self.x.val, 0) * self.alpha.val, self.beta.val)
-        return x + y
+        # Splitting into a positive and a negative half and adding them back together
+        # only works when at most one half is non-zero, which stops being true once
+        # beta is negative and clamps both of them. Apply the documented formula
+        # directly instead.
+        return np.minimum(
+            np.where(self.x.val >= 0, self.x.val, self.x.val * self.alpha.val),
+            self.beta.val,
+        )
 
 
 @register_op
diff --git a/coremltools/converters/mil/mil/ops/tests/iOS14/test_activation.py b/coremltools/converters/mil/mil/ops/tests/iOS14/test_activation.py
index c80f90ddb..e851853df 100644
--- a/coremltools/converters/mil/mil/ops/tests/iOS14/test_activation.py
+++ b/coremltools/converters/mil/mil/ops/tests/iOS14/test_activation.py
@@ -55,6 +55,20 @@ def test_builder_eval(self):
         y = np.minimum(np.minimum(x_val, 0) * 2.0, 1.0)
         np.testing.assert_allclose(x + y, v.val, atol=1e-04, rtol=1e-05)
 
+    @ssa_fn
+    def test_builder_eval_negative_beta(self):
+        """
+        Constant folding must agree with the runtime contract
+        ``f(x) = min((x >= 0 ? x : alpha * x), beta)`` for a negative beta too.
+        The alpha / beta combinations here match the ones the neural network backend
+        test for this layer already covers (test_numpy_nn_layers.test_clamped_relu_cpu).
+        """
+        x_val = np.arange(-20, 20, dtype=np.float32)
+        for alpha, beta in itertools.product([0.0, 2.0, -3.0], [7.0, -8.0]):
+            v = mb.clamped_relu(x=x_val, alpha=alpha, beta=beta)
+            expected = np.minimum(beta, np.where(x_val >= 0, x_val, x_val * alpha))
+            np.testing.assert_allclose(expected, v.val, atol=1e-04, rtol=1e-05)
+
     @pytest.mark.parametrize(
         "compute_unit, backend, dim, alpha, beta",
         itertools.product(compute_units, backends, [2, 4, 8], [2.0, 3.0], [4.0, 5.0]),