diff --git a/src/array_api_extra/_lib/_testing.py b/src/array_api_extra/_lib/_testing.py
index cc2306bd..d2a57c86 100644
--- a/src/array_api_extra/_lib/_testing.py
+++ b/src/array_api_extra/_lib/_testing.py
@@ -37,7 +37,7 @@ def _check_ns_shape_dtype(
     check_dtype: bool,
     check_shape: bool,
     check_scalar: bool,
-) -> ModuleType:  # numpydoc ignore=RT03
+) -> tuple[Array, Array, ModuleType]:  # numpydoc ignore=RT03
     """
     Assert that namespace, shape and dtype of the two arrays match.
 
@@ -55,24 +55,35 @@ def _check_ns_shape_dtype(
 
     Returns
     -------
-    Arrays namespace.
+    Actual array, desired array, and their namespace.
     """
-    actual_xp = array_namespace(actual)  # Raises on scalars and lists
+    actual_xp = array_namespace(actual)  # Raises on Python scalars and lists
     desired_xp = array_namespace(desired)
 
     msg = f"namespaces do not match: {actual_xp} != f{desired_xp}"
     assert actual_xp == desired_xp, msg
 
+    if is_numpy_namespace(actual_xp) and check_scalar:
+        # only NumPy distinguishes between scalars and arrays; we do if check_scalar.
+        _msg = (
+            "array-ness does not match:\n Actual: "
+            f"{type(actual)}\n Desired: {type(desired)}"
+        )
+        assert np.isscalar(actual) == np.isscalar(desired), _msg
+
     # Dask uses nan instead of None for unknown shapes
     actual_shape = cast(tuple[float, ...], actual.shape)
     desired_shape = cast(tuple[float, ...], desired.shape)
     assert None not in actual_shape  # Requires explicit support
     assert None not in desired_shape
+
     if is_dask_namespace(desired_xp):
         if any(math.isnan(i) for i in actual_shape):
-            actual_shape = actual.compute().shape  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
+            actual.compute_chunk_sizes()  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
+            actual_shape = cast(tuple[float, ...], actual.shape)
         if any(math.isnan(i) for i in desired_shape):
-            desired_shape = desired.compute().shape  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
+            desired.compute_chunk_sizes()  # type: ignore[attr-defined]  # pyright: ignore[reportAttributeAccessIssue]
+            desired_shape = cast(tuple[float, ...], desired.shape)
 
     if check_shape:
         msg = f"shapes do not match: {actual_shape} != f{desired_shape}"
@@ -82,24 +93,16 @@ def _check_ns_shape_dtype(
         # np.testing.assert_array_equal etc even when strict=False, but not for
         # non-materializable arrays.
         # This check excludes 0d arrays as they are special-cased in NumPy.
-        actual_size = math.prod(actual_shape)  # pyright: ignore[reportUnknownArgumentType]
-        desired_size = math.prod(desired_shape)  # pyright: ignore[reportUnknownArgumentType]
+        actual_size = math.prod(actual_shape)
+        desired_size = math.prod(desired_shape)
         msg = f"sizes do not match: {actual_size} != f{desired_size}"
         assert actual_size == desired_size, msg
 
     if check_dtype:
         msg = f"dtypes do not match: {actual.dtype} != {desired.dtype}"
         assert actual.dtype == desired.dtype, msg
-
-    if is_numpy_namespace(actual_xp) and check_scalar:
-        # only NumPy distinguishes between scalars and arrays; we do if check_scalar.
-        _msg = (
-            "array-ness does not match:\n Actual: "
-            f"{type(actual)}\n Desired: {type(desired)}"
-        )
-        assert np.isscalar(actual) == np.isscalar(desired), _msg
-
-    return desired_xp
+    desired = desired_xp.broadcast_to(desired, actual_shape)
+    return actual, desired, desired_xp
 
 
 def _is_materializable(x: Array) -> bool:
@@ -169,7 +172,9 @@ def xp_assert_equal(
     xp_assert_close : Similar function for inexact equality checks.
     numpy.testing.assert_array_equal : Similar function for NumPy arrays.
     """
-    xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar)
+    actual, desired, xp = _check_ns_shape_dtype(
+        actual, desired, check_dtype, check_shape, check_scalar
+    )
     if not _is_materializable(actual):
         return
     actual_np = as_numpy_array(actual, xp=xp)
@@ -211,7 +216,7 @@ def xp_assert_less(
     xp_assert_close : Similar function for inexact equality checks.
     numpy.testing.assert_array_equal : Similar function for NumPy arrays.
     """
-    xp = _check_ns_shape_dtype(x, y, check_dtype, check_shape, check_scalar)
+    x, y, xp = _check_ns_shape_dtype(x, y, check_dtype, check_shape, check_scalar)
     if not _is_materializable(x):
         return
     x_np = as_numpy_array(x, xp=xp)
@@ -267,7 +272,9 @@ def xp_assert_close(
     -----
     The default `atol` and `rtol` differ from `xp.all(xpx.isclose(a, b))`.
     """
-    xp = _check_ns_shape_dtype(actual, desired, check_dtype, check_shape, check_scalar)
+    actual, desired, xp = _check_ns_shape_dtype(
+        actual, desired, check_dtype, check_shape, check_scalar
+    )
     if not _is_materializable(actual):
         return