diff --git a/array_api_strict/_data_type_functions.py b/array_api_strict/_data_type_functions.py
index 16795fc..c3c8462 100644
--- a/array_api_strict/_data_type_functions.py
+++ b/array_api_strict/_data_type_functions.py
@@ -204,35 +204,31 @@ def result_type(
     # required by the spec rather than using np.result_type. NumPy implements
     # too many extra type promotions like int64 + uint64 -> float64, and does
     # value-based casting on scalar arrays.
-    A = []
+    dtypes = []
     scalars = []
     for a in arrays_and_dtypes:
-        if isinstance(a, Array):
-            a = a.dtype
+        if isinstance(a, DType):
+            dtypes.append(a)
+        elif isinstance(a, Array):
+            dtypes.append(a.dtype)
         elif isinstance(a, (bool, int, float, complex)):
             scalars.append(a)
-        elif isinstance(a, np.ndarray) or a not in _all_dtypes:
-            raise TypeError("result_type() inputs must be array_api arrays or dtypes")
-        A.append(a)
-
-    # remove python scalars
-    B = [a for a in A if not isinstance(a, (bool, int, float, complex))]
+        else:
+            raise TypeError(
+                "result_type() inputs must be Array API arrays, dtypes, or scalars"
+            )
 
-    if len(B) == 0:
+    if not dtypes:
         raise ValueError("at least one array or dtype is required")
-    elif len(B) == 1:
-        result = B[0]
-    else:
-        t = B[0]
-        for t2 in B[1:]:
-            t = _result_type(t, t2)
-        result = t
+    result = dtypes[0]
+    for t2 in dtypes[1:]:
+        result = _result_type(result, t2)
 
-    if len(scalars) == 0:
+    if not scalars:
         return result
 
     if get_array_api_strict_flags()['api_version'] <= '2023.12':
-        raise TypeError("result_type() inputs must be array_api arrays or dtypes")
+        raise TypeError("result_type() inputs must be Array API arrays or dtypes")
 
     # promote python scalars given the result_type for all arrays/dtypes
     from ._creation_functions import empty