diff --git a/python/cudf/cudf/core/column/column.py b/python/cudf/cudf/core/column/column.py
index 22f8d27f9e8..f22add316e6 100644
--- a/python/cudf/cudf/core/column/column.py
+++ b/python/cudf/cudf/core/column/column.py
@@ -502,7 +502,8 @@ def _wrap_binop_normalization(self, other):
         if other is NA or other is None:
             return cudf.Scalar(other, dtype=self.dtype)
         if isinstance(other, np.ndarray) and other.ndim == 0:
-            other = other.item()
+            # Try and maintain the dtype
+            other = other.dtype.type(other.item())
         return self.normalize_binop_value(other)
 
     def _scatter_by_slice(
diff --git a/python/cudf/cudf/core/column/numerical.py b/python/cudf/cudf/core/column/numerical.py
index f126f47c3c2..7ca4c40f674 100644
--- a/python/cudf/cudf/core/column/numerical.py
+++ b/python/cudf/cudf/core/column/numerical.py
@@ -35,7 +35,7 @@
     is_number,
     is_scalar,
 )
-from cudf.core.buffer import Buffer, as_buffer, cuda_array_interface_wrapper
+from cudf.core.buffer import Buffer, cuda_array_interface_wrapper
 from cudf.core.column import (
     ColumnBase,
     as_column,
@@ -274,7 +274,7 @@ def nans_to_nulls(self: NumericalColumn) -> NumericalColumn:
 
     def normalize_binop_value(
         self, other: ScalarLike
-    ) -> Union[ColumnBase, ScalarLike]:
+    ) -> Union[ColumnBase, cudf.Scalar]:
         if isinstance(other, ColumnBase):
             if not isinstance(other, NumericalColumn):
                 return NotImplemented
@@ -285,25 +285,20 @@ def normalize_binop_value(
             # expensive device-host transfer just to
             # adjust the dtype
             other = other.value
-        other_dtype = np.min_scalar_type(other)
+        try:
+            # Try and use the dtype of the incoming object
+            other_dtype = other.dtype
+        except AttributeError:
+            # Otherwise fall back to numpy's type deduction scheme.
+            other_dtype = np.result_type(other)
+
         if other_dtype.kind in {"b", "i", "u", "f"}:
-            if isinstance(other, cudf.Scalar):
-                return other
-            other_dtype = np.promote_types(self.dtype, other_dtype)
-            if other_dtype == np.dtype("float16"):
-                other_dtype = cudf.dtype("float32")
-                other = other_dtype.type(other)
+            common_dtype = np.promote_types(self.dtype, other_dtype)
+            if common_dtype == np.dtype("float16"):
+                common_dtype = cudf.dtype("float32")
             if self.dtype.kind == "b":
-                other_dtype = min_signed_type(other)
-            if np.isscalar(other):
-                return cudf.dtype(other_dtype).type(other)
-            else:
-                ary = full(len(self), other, dtype=other_dtype)
-                return column.build_column(
-                    data=as_buffer(ary),
-                    dtype=ary.dtype,
-                    mask=self.mask,
-                )
+                common_dtype = min_signed_type(other)
+            return cudf.Scalar(other, dtype=common_dtype)
         else:
             return NotImplemented
 
diff --git a/python/cudf/cudf/tests/test_series.py b/python/cudf/cudf/tests/test_series.py
index c0b99f56238..c9587438b09 100644
--- a/python/cudf/cudf/tests/test_series.py
+++ b/python/cudf/cudf/tests/test_series.py
@@ -1951,3 +1951,9 @@ def test_set_bool_error(dtype, bool_scalar):
         lfunc_args_and_kwargs=([bool_scalar],),
         rfunc_args_and_kwargs=([bool_scalar],),
     )
+
+
+def test_int64_equality():
+    s = cudf.Series(np.asarray([2**63 - 10, 2**63 - 100], dtype=np.int64))
+    assert (s != np.int64(2**63 - 1)).all()
+    assert (s != cudf.Scalar(2**63 - 1, dtype=np.int64)).all()