diff --git a/CHANGELOG.md b/CHANGELOG.md
index d20d4ef..8565870 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -9,6 +9,9 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 ### Added
 * Added `mkl_fft` patching for NumPy, with `mkl_fft` context manager, `is_patched` query, and `patch_numpy_fft` and `restore_numpy_fft` calls to replace `numpy.fft` calls with calls from `mkl_fft.interfaces.numpy_fft` [gh-224](https://github.com/IntelPython/mkl_fft/pull/224)
 
+### Changed
+* In `mkl_fft.fftn` and `mkl_fft.ifftn`, improved checking of the shape argument `s` to use faster direct transforms more often. This makes performance more consistent between `mkl_fft.fftn/ifftn` and `mkl.interfaces`. [gh-283](https://github.com/IntelPython/mkl_fft/pull/283)
+
 ### Removed
 * Dropped support for Python 3.9 [gh-243](https://github.com/IntelPython/mkl_fft/pull/243)
 
diff --git a/mkl_fft/_fft_utils.py b/mkl_fft/_fft_utils.py
index ad6a055..c0fda37 100644
--- a/mkl_fft/_fft_utils.py
+++ b/mkl_fft/_fft_utils.py
@@ -43,11 +43,12 @@ def _check_norm(norm):
         )
 
 
-def _check_shapes_for_direct(xs, shape, axes):
+def _check_shapes_for_direct(xs, shape, axes, check_complimentary=False):
     if len(axes) > 7:  # Intel MKL supports up to 7D
         return False
-    if not (len(xs) == len(shape)):
-        # full-dimensional transform
+    if not (len(xs) == len(shape)) and not check_complimentary:
+        # full-dimensional transform is required for direct,
+        # but less than full is OK for complimentary.
         return False
     if not (len(set(axes)) == len(axes)):
         # repeated axes
@@ -382,6 +383,7 @@ def _c2c_fftnd_impl(
     if direction not in [-1, +1]:
         raise ValueError("Direction of FFT should +1 or -1")
 
+    _complementary = s is None
     valid_dtypes = [np.complex64, np.complex128, np.float32, np.float64]
     # _direct_fftnd requires complex type, and full-dimensional transform
     if isinstance(x, np.ndarray) and x.size != 0 and x.ndim > 1:
@@ -392,6 +394,12 @@ def _c2c_fftnd_impl(
             xs, xa = _cook_nd_args(x, s, axes)
             if _check_shapes_for_direct(xs, x.shape, xa):
                 _direct = True
+            # See if s matches the shape of x along the given axes.
+            # If it does, we can use _iter_complementary rather than _iter_fftnd.
+            if _check_shapes_for_direct(
+                xs, x.shape, xa, check_complimentary=True
+            ):
+                _complementary = True
         _direct = _direct and x.dtype in valid_dtypes
     else:
         _direct = False
@@ -404,7 +412,7 @@ def _c2c_fftnd_impl(
             out=out,
         )
     else:
-        if s is None and x.dtype in valid_dtypes:
+        if _complementary and x.dtype in valid_dtypes:
             x = np.asarray(x)
             if out is None:
                 res = np.empty_like(x, dtype=_output_dtype(x.dtype))
diff --git a/mkl_fft/tests/test_fftnd.py b/mkl_fft/tests/test_fftnd.py
index 4f04a11..c1808aa 100644
--- a/mkl_fft/tests/test_fftnd.py
+++ b/mkl_fft/tests/test_fftnd.py
@@ -264,6 +264,30 @@ def test_s_axes_out(dtype, s, axes, func):
     assert_allclose(r1, r2, rtol=rtol, atol=atol)
 
 
+@requires_numpy_2
+@pytest.mark.parametrize("dtype", [complex, float])
+@pytest.mark.parametrize("axes", [(1, 2, 3), (-1, -2, -3), [2, 1, 3]])
+@pytest.mark.parametrize("func", ["fftn", "ifftn", "rfftn"])
+def test_s_none_vs_s_full(dtype, axes, func):
+    shape = (2, 30, 20, 10)
+    if dtype is complex and func != "rfftn":
+        x = np.random.random(shape) + 1j * np.random.random(shape)
+    else:
+        x = np.random.random(shape)
+
+    implied_s = [shape[ax] for ax in axes]
+    if func == "irfftn":
+        implied_s[-1] = 2 * (implied_s[-1] - 1)
+
+    r1 = getattr(np.fft, func)(x, axes=axes)
+    r2 = getattr(mkl_fft, func)(x, axes=axes)
+    r3 = getattr(mkl_fft, func)(x, s=implied_s, axes=axes)
+
+    rtol, atol = _get_rtol_atol(x)
+    assert_allclose(r1, r2, rtol=rtol, atol=atol)
+    assert_allclose(r1, r3, rtol=rtol, atol=atol)
+
+
 @pytest.mark.parametrize("dtype", [complex, float])
 @pytest.mark.parametrize("axes", [(2, 0, 2, 0), (0, 1, 1), (2, 0, 1, 3, 2, 1)])
 @pytest.mark.parametrize("func", ["rfftn", "irfftn"])