diff --git a/pandas/core/apply.py b/pandas/core/apply.py
index b0f357a1d8db3..c03f1a268906e 100644
--- a/pandas/core/apply.py
+++ b/pandas/core/apply.py
@@ -91,6 +91,9 @@ def frame_apply(
     elif axis == 1:
         klass = FrameColumnApply
 
+    _, func, _, _ = reconstruct_func(func, **kwargs)
+    assert func is not None
+
     return klass(
         obj,
         func,
@@ -547,7 +550,20 @@ def apply_multiple(self) -> DataFrame | Series:
         result: Series, DataFrame, or None
             Result when self.f is a list-like or dict-like, None otherwise.
         """
-        return self.obj.aggregate(self.f, self.axis, *self.args, **self.kwargs)
+        if self.axis == 1 and isinstance(self.obj, ABCDataFrame):
+            return self.obj.T.apply(self.f, 0, args=self.args, **self.kwargs).T
+
+        func = self.f
+        kwargs = self.kwargs
+
+        if is_dict_like(func):
+            result = self.agg_dict_like()
+        else:
+            result = self.agg_list_like()
+
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
+
+        return result
 
     def normalize_dictlike_arg(
         self, how: str, obj: DataFrame | Series, func: AggFuncTypeDict
@@ -1439,6 +1455,26 @@ def relabel_result(
     return reordered_result_in_dict
 
 
+def reconstruct_and_relabel_result(result, func, **kwargs) -> DataFrame | Series:
+    from pandas import DataFrame
+
+    relabeling, func, columns, order = reconstruct_func(func, **kwargs)
+
+    if relabeling:
+        # This is to keep the order to columns occurrence unchanged, and also
+        # keep the order of new columns occurrence unchanged
+
+        # For the return values of reconstruct_func, if relabeling is
+        # False, columns and order will be None.
+        assert columns is not None
+        assert order is not None
+
+        result_in_dict = relabel_result(result, func, columns, order)
+        result = DataFrame(result_in_dict, index=columns)
+
+    return result
+
+
 # TODO: Can't use, because mypy doesn't like us setting __name__
 #   error: "partial[Any]" has no attribute "__name__"
 # the type is:
diff --git a/pandas/core/frame.py b/pandas/core/frame.py
index 8afb3ee96ba94..ac7667afbea5f 100644
--- a/pandas/core/frame.py
+++ b/pandas/core/frame.py
@@ -122,10 +122,7 @@
     roperator,
 )
 from pandas.core.accessor import CachedAccessor
-from pandas.core.apply import (
-    reconstruct_func,
-    relabel_result,
-)
+from pandas.core.apply import reconstruct_and_relabel_result
 from pandas.core.array_algos.take import take_2d_multi
 from pandas.core.arraylike import OpsMixin
 from pandas.core.arrays import (
@@ -9579,23 +9576,9 @@ def aggregate(self, func=None, axis: Axis = 0, *args, **kwargs):
 
         axis = self._get_axis_number(axis)
 
-        relabeling, func, columns, order = reconstruct_func(func, **kwargs)
-
         op = frame_apply(self, func=func, axis=axis, args=args, kwargs=kwargs)
         result = op.agg()
-
-        if relabeling:
-            # This is to keep the order to columns occurrence unchanged, and also
-            # keep the order of new columns occurrence unchanged
-
-            # For the return values of reconstruct_func, if relabeling is
-            # False, columns and order will be None.
-            assert columns is not None
-            assert order is not None
-
-            result_in_dict = relabel_result(result, func, columns, order)
-            result = DataFrame(result_in_dict, index=columns)
-
+        result = reconstruct_and_relabel_result(result, func, **kwargs)
         return result
 
     agg = aggregate
diff --git a/pandas/tests/apply/test_frame_apply.py b/pandas/tests/apply/test_frame_apply.py
index 80292ac6641ac..fc8b57d26a5be 100644
--- a/pandas/tests/apply/test_frame_apply.py
+++ b/pandas/tests/apply/test_frame_apply.py
@@ -37,6 +37,13 @@ def test_apply(float_frame):
         assert result.index is float_frame.index
 
 
+@pytest.mark.parametrize("axis", [0, 1])
+def test_apply_args(float_frame, axis):
+    result = float_frame.apply(lambda x, y: x + y, axis, args=(1,))
+    expected = float_frame + 1
+    tm.assert_frame_equal(result, expected)
+
+
 def test_apply_categorical_func():
     # GH 9573
     df = DataFrame({"c0": ["A", "A", "B", "B"], "c1": ["C", "C", "D", "D"]})