diff --git a/python/metatensor-operations/metatensor/operations/_dispatch.py b/python/metatensor-operations/metatensor/operations/_dispatch.py
index 8befd6554..1f6f4efff 100644
--- a/python/metatensor-operations/metatensor/operations/_dispatch.py
+++ b/python/metatensor-operations/metatensor/operations/_dispatch.py
@@ -148,7 +148,9 @@ def argsort_labels_values(labels_values, reverse: bool = False):
         # so we temporary do this trick. this will be fixed with issue #366
         max_int = torch.max(labels_values)
         idx = torch.sum(
-            max_int ** torch.arange(labels_values.shape[1]) * labels_values, dim=1
+            max_int ** torch.arange(labels_values.shape[1])
+            * labels_values.flip(dims=[1]),
+            dim=1,
         )
         return torch.argsort(idx, dim=-1, descending=reverse)
     elif isinstance(labels_values, np.ndarray):
diff --git a/python/metatensor-torch/tests/operations/sort.py b/python/metatensor-torch/tests/operations/sort.py
index 36a4c765e..79782680c 100644
--- a/python/metatensor-torch/tests/operations/sort.py
+++ b/python/metatensor-torch/tests/operations/sort.py
@@ -305,3 +305,125 @@ def test_sort_descending(tensor, tensor_sorted_descending):
         tensor_sorted_descending.block(0),
         metatensor.torch.sort_block(tensor.block(0), descending=True),
     )
+
+
+def test_high_numb():
+    tensor = TensorMap(
+        keys=Labels(
+            names=["a", "b"],
+            values=torch.tensor([[2, 1], [1, 0]]),
+        ),
+        blocks=[
+            TensorBlock(
+                values=torch.tensor([[1, 2], [3, 4], [5, 6]], dtype=torch.float32),
+                samples=Labels(
+                    names=["s1", "s2", "s3"],
+                    values=torch.tensor(
+                        [
+                            [0, 1, 2],
+                            [2, 3, 4],
+                            [1, 5, 7],
+                        ]
+                    ),
+                ),
+                components=[],
+                properties=Labels(
+                    names=["p1", "p2"],
+                    values=torch.tensor(
+                        [
+                            [100, 0],
+                            [5, 7000],
+                        ]
+                    ),
+                ),
+            ),
+            TensorBlock(
+                values=torch.tensor(
+                    [[2.2, 3.1, 4.1], [2.2, 1.1, 2.1], [2.2, 5.1, 6.1]]
+                ),
+                samples=Labels(
+                    names=["s1", "s2", "s3"],
+                    values=torch.tensor(
+                        [
+                            [0, 2, 2],
+                            [0, 1, 2],
+                            [1, 5, 7],
+                        ]
+                    ),
+                ),
+                components=[],
+                properties=Labels(
+                    names=["p1", "p2"],
+                    values=torch.tensor(
+                        [
+                            [5, 10],
+                            [5, 5],
+                            [5, 6],
+                        ]
+                    ),
+                ),
+            ),
+        ],
+    )
+
+    tensor_order = TensorMap(
+        keys=Labels(
+            names=["a", "b"],
+            values=torch.tensor([[1, 0], [2, 1]]),
+        ),
+        blocks=[
+            TensorBlock(
+                values=torch.tensor(
+                    [[1.1, 2.1, 2.2], [3.1, 4.1, 2.2], [5.1, 6.1, 2.2]]
+                ),
+                samples=Labels(
+                    names=["s1", "s2", "s3"],
+                    values=torch.tensor(
+                        [
+                            [0, 1, 2],
+                            [0, 2, 2],
+                            [1, 5, 7],
+                        ]
+                    ),
+                ),
+                components=[],
+                properties=Labels(
+                    names=["p1", "p2"],
+                    values=torch.tensor(
+                        [
+                            [5, 5],
+                            [5, 6],
+                            [5, 10],
+                        ]
+                    ),
+                ),
+            ),
+            TensorBlock(
+                values=torch.tensor([[2, 1], [6, 5],[4, 3]], dtype=torch.float32),
+                samples=Labels(
+                    names=["s1", "s2", "s3"],
+                    values=torch.tensor(
+                        [
+                            [0, 1, 2],
+                            [1, 5, 7],
+                            [2, 3, 4],
+                            
+                        ]
+                    ),
+                ),
+                components=[],
+                properties=Labels(
+                    names=["p1", "p2"],
+                    values=torch.tensor(
+                        [
+                            [5, 7000],
+                            [100, 0],
+                            
+                        ]
+                    ),
+                ),
+            ),
+        ],
+    )
+    sorted = metatensor.torch.sort(tensor)
+    metatensor.torch.allclose_raise(sorted, tensor_order)