diff --git a/test/test_ops.py b/test/test_ops.py
index c346b4cdbd..d71e8247e9 100644
--- a/test/test_ops.py
+++ b/test/test_ops.py
@@ -392,6 +392,20 @@ class TestOps(unittest.TestCase):
     helper_test_op([(20,30)], lambda x: torch.cumsum(x, dim=1), lambda x: Tensor.cumsum(x, axis=1), atol=1e-6)
     helper_test_op([(20,30,40)], lambda x: torch.cumsum(x, dim=2), lambda x: Tensor.cumsum(x, axis=2), atol=1e-6)
     helper_test_op([(20,30,40)], lambda x: torch.cumsum(x, dim=-1), lambda x: Tensor.cumsum(x, axis=-1), atol=1e-6)
+
+  def test_argmax(self):
+    self.assertEqual(torch.Tensor([2,2]).argmax().numpy(), Tensor([2,2]).argmax().numpy()) # check if returns first index for same max
+    helper_test_op([(10,20)], lambda x: x.argmax(), lambda x: x.argmax(), forward_only=True)
+    helper_test_op([(10,20)], lambda x: x.argmax(0, False), lambda x: x.argmax(0, False), forward_only=True)
+    helper_test_op([(10,20)], lambda x: x.argmax(1, False), lambda x: x.argmax(1, False), forward_only=True)
+    helper_test_op([(10,20)], lambda x: x.argmax(1, True), lambda x: x.argmax(1, True), forward_only=True)
+  def test_argmin(self):
+    self.assertEqual(torch.Tensor([2, 2]).argmin().numpy(), Tensor([2, 2]).argmin().numpy())
+    helper_test_op([(10,20)], lambda x: x.argmin(), lambda x: x.argmin(), forward_only=True)
+    helper_test_op([(10,20)], lambda x: x.argmin(0, False), lambda x: x.argmin(0, False), forward_only=True)
+    helper_test_op([(10,20)], lambda x: x.argmin(1, False), lambda x: x.argmin(1, False), forward_only=True)
+    helper_test_op([(10,20)], lambda x: x.argmin(1, True), lambda x: x.argmin(1, True), forward_only=True)
+
   def test_matmul_simple(self):
     helper_test_op([(4), (4,4)], lambda x,y: x.matmul(y), Tensor.dot, atol=1e-4)
   def test_matmul(self):
diff --git a/tinygrad/tensor.py b/tinygrad/tensor.py
index 9788bfc032..5e3824eaf1 100644
--- a/tinygrad/tensor.py
+++ b/tinygrad/tensor.py
@@ -449,6 +449,14 @@ class Tensor:
     m, _, ss = self._softmax(axis)
     return m - ss.log()
 
+  def argmax(self, axis=None, keepdim=False):
+    if axis is None: return prod(self.shape) - ((self == self.max(axis)).flatten() * Tensor.arange(prod(self.shape)-1,-1,-1)).max() - 1
+    axis = axis + self.ndim if axis < 0 else axis
+    m = self == (self.max(axis=axis, keepdim=keepdim) if keepdim else self.max(axis=axis, keepdim=keepdim).unsqueeze(axis))
+    idx = m * Tensor.arange(self.shape[axis]-1,-1,-1).reshape(*[1]*axis, self.shape[axis], *[1]*(self.ndim-(axis+1)))
+    return self.shape[axis]-idx.max(axis=axis, keepdim=keepdim)-1
+  def argmin(self, axis=None, keepdim=False): return (-self).argmax(axis=axis, keepdim=keepdim)
+
   # ***** processing ops *****
 
   def _pool(self, k_:Tuple[int, ...], stride:Union[Tuple[int, ...], int]=1, dilation:Union[Tuple[int, ...], int]=1) -> Tensor: