diff --git a/test/test_dtype.py b/test/test_dtype.py
index 13438d52a8..18953b6117 100644
--- a/test/test_dtype.py
+++ b/test/test_dtype.py
@@ -439,6 +439,13 @@ class TestTypeSpec(unittest.TestCase):
     assert Tensor([0, 1], dtype=dtype).argmin().dtype == dtypes.int32
     assert Tensor([0, 1], dtype=dtype).multinomial().dtype == dtypes.int32
 
+  @given(strat.sampled_from(core_dtypes), strat.sampled_from(dtype_ints))
+  def test_tensor_indexing_returns_same_dtype(self, data_dtype, indices_dtype):
+    X_data =  Tensor.rand(60000, 1, 28, 28, dtype=data_dtype)
+    indices =  Tensor.randint(512, high=X_data.shape[0]).cast(indices_dtype)
+    X = X_data[indices]
+    assert X.dtype == X_data.dtype
+
 class TestTypePromotion(unittest.TestCase):
   @given(strat.sampled_from(core_dtypes))
   def test_self_promo_to_self(self, dtype):
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 7a16a93a0e..76318a6f50 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -502,12 +502,12 @@ class Tensor:
         masks.append(i == a)
 
       # reduce masks to 1 mask
-      mask = functools.reduce(lambda x,y: x.mul(y), masks)
+      mask: Tensor = functools.reduce(lambda x,y: x.mul(y), masks)
 
       # inject 1's for the extra dims added in create masks
       sh = ret.shape[:first_dim] + (1,) * len(big_shape) + ret.shape[first_dim:]
       # sum reduce the extra dims introduced in create masks
-      ret = (ret.reshape(sh) * mask).sum(tuple(i + len(big_shape) for i in idx.keys()))
+      ret = (ret.reshape(sh) * mask).sum(tuple(i + len(big_shape) for i in idx.keys()), acc_dtype=ret.dtype)
 
       # special permute case
       if first_dim != 0 and len(idx) != 1 and tuple(idx.keys()) != tuple(range(first_dim, last_dim+1)):