diff --git a/python/tvm/relay/frontend/onnx.py b/python/tvm/relay/frontend/onnx.py
index 4444b15dfb12..ba2c6b4b54e7 100644
--- a/python/tvm/relay/frontend/onnx.py
+++ b/python/tvm/relay/frontend/onnx.py
@@ -1468,18 +1468,26 @@ class Unsqueeze(OnnxOpConverter):
     """Operator converter for Unsqueeze."""
 
     @classmethod
-    def _impl_v1(cls, inputs, attr, params):
-        axes = sorted(attr["axes"])
+    def run_calculation(cls, tensor, axes):
+        axes = sorted(axes)
         for axis in axes:
-            inputs[0] = _op.expand_dims(inputs[0], axis=axis, num_newaxis=1)
-        return inputs[0]
+            tensor = _op.expand_dims(tensor, axis=axis, num_newaxis=1)
+        return tensor
 
     @classmethod
-    def _impl_v12(cls, inputs, attr, params):
+    def _impl_v1(cls, inputs, attr, params):
+        return cls.run_calculation(inputs[0], attr["axes"])
+
+    @classmethod
+    def _impl_v13(cls, inputs, attr, params):
+        if isinstance(inputs[1], _expr.Constant):
+            constant_axes = list(inputs[1].data.numpy())
+            constant_axes = list(map(int, constant_axes))
+            return cls.run_calculation(inputs[0], constant_axes)
+
         rank_input = len(infer_type(inputs[0]).checked_type.shape)
         num_new_axis = int(infer_type(inputs[1]).checked_type.shape[0])
         axes = relay.split(inputs[1], num_new_axis).astuple()
-
         result = inputs[0]
 
         # TODO (AndrewZhaoLuo): investigate performance issues with consecutive