diff --git a/test/test_setitem.py b/test/test_setitem.py
index 967acc29f1..54ae9007af 100644
--- a/test/test_setitem.py
+++ b/test/test_setitem.py
@@ -165,6 +165,17 @@ class TestSetitem(unittest.TestCase):
     t[idx] = val
     self.assertEqual(t.tolist(), [val]*idx_size+[idx_size])
 
+  def test_setitem_advanced_indexing(self):
+    # Example from https://numpy.org/doc/stable/user/basics.indexing.html#combining-advanced-and-basic-indexing
+    t = Tensor.zeros(10,20,30,40,50).contiguous()
+    ind_1 = Tensor([5,3,7,8])
+    ind_2 = Tensor([[[0],[1],[2]],[[3],[4],[5]]])
+    v = Tensor.arange(2*3*4*10*30*50).reshape(2,3,4,10,30,50)
+    t[:, ind_1, :, ind_2, :] = v
+    n = np.zeros((10,20,30,40,50))
+    n[:, ind_1.numpy(), :, ind_2.numpy(), :] = v.numpy()
+    np.testing.assert_allclose(t.numpy(), n)
+
 class TestWithGrad(unittest.TestCase):
   def test_no_requires_grad_works(self):
     z = Tensor.rand(8, 8)
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index fb37bd7b18..b83d751282 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -1220,8 +1220,8 @@ class Tensor(MathTrait):
       x = (mask.where(x.reshape(reshape_arg), 0)).sum(sum_axis:=tuple(d + len(big_shape) for d in dims), dtype=x.dtype)
 
       # special permute case
-      if dims[0] != 0 and len(dims) != 1 and tuple(dims) != tuple(range(dims[0], dims[-1]+1)):
-        x = x.permute(*range(dims[0], dims[0]+len(big_shape)), *range(0, dims[0]), *range(dims[0]+len(big_shape), x.ndim))
+      if (permuted := dims[0] != 0 and len(dims) != 1 and tuple(dims) != tuple(range(dims[0], dims[-1]+1))):
+        mask, x = (y.permute(*range(dims[0], dims[0]+len(big_shape)), *range(0, dims[0]), *range(dims[0]+len(big_shape), y.ndim)) for y in (mask, x))
 
       # for advanced setitem, returns whole tensor with indices replaced
       if v is not None:
@@ -1229,7 +1229,7 @@ class Tensor(MathTrait):
         # add back reduced dims from sum
         for dim in sum_axis: vb = vb.unsqueeze(dim)
         # run _masked_setitem on tuple of axis that is to be reduced to match self.shape
-        x = _masked_setitem(self, vb, mask, tuple(range(dims[0], dims[0] + len(big_shape))))
+        x = _masked_setitem(self, vb, mask, tuple(range((start := dims[0] if not permuted else 0), start + len(big_shape))))
 
     return x