diff --git a/python/tvm/relax/frontend/onnx/onnx_frontend.py b/python/tvm/relax/frontend/onnx/onnx_frontend.py
index 65bd5bfe1a2f..7e8616f65f7b 100644
--- a/python/tvm/relax/frontend/onnx/onnx_frontend.py
+++ b/python/tvm/relax/frontend/onnx/onnx_frontend.py
@@ -1561,6 +1561,7 @@ class Reshape(OnnxOpConverter):
     def _impl_v13(cls, bb, inputs, attr, params):
         data = inputs[0]
         new_shape = get_constant(inputs[1], params)
+        allowzero = attr.get("allowzero", 0)
 
         if isinstance(data, relax.ShapeExpr):
             # Preserve identity flatten for shape values to keep shape-specialized
@@ -1574,10 +1575,23 @@ def _impl_v13(cls, bb, inputs, attr, params):
             data = bb.normalize(relax.op.shape_to_tensor(data))
 
         if isinstance(data, relax.Constant) and isinstance(new_shape, relax.Constant):
-            out = _np.reshape(data.data.numpy(), new_shape.data.numpy().tolist())
+            data_array = data.data.numpy()
+            new_shape_values = new_shape.data.numpy().tolist()
+            if not allowzero:
+                new_shape_values = [
+                    data_array.shape[i] if dim == 0 else dim
+                    for i, dim in enumerate(new_shape_values)
+                ]
+            out = _np.reshape(data_array, new_shape_values)
             return relax.const(out, out.dtype)
         if isinstance(new_shape, relax.Constant):
-            new_shape = new_shape.data.numpy().tolist()
+            new_shape_values = new_shape.data.numpy().tolist()
+            if allowzero and 0 in new_shape_values:
+                new_shape = _tensor_to_shape_expr(
+                    bb, new_shape, len(new_shape_values), "reshape_dim"
+                )
+            else:
+                new_shape = new_shape_values
         out = relax.op.reshape(data, new_shape)
         return out
 
diff --git a/tests/python/relax/test_frontend_onnx.py b/tests/python/relax/test_frontend_onnx.py
index 673c25bcddd8..09f3a7b01d88 100644
--- a/tests/python/relax/test_frontend_onnx.py
+++ b/tests/python/relax/test_frontend_onnx.py
@@ -2274,6 +2274,71 @@ def main(
     verify_reshape([7, 32, 32, 8], [0, 32, 32, 8], [7, 32, 32, 8], ExpectedCopyInputDim)
 
 
+def test_reshape_constant_zero_copy_dimension():
+    data = np.arange(6, dtype="float32").reshape(2, 3)
+    reshape_node = helper.make_node("Reshape", ["data", "shape"], ["reshaped"])
+    graph = helper.make_graph(
+        [reshape_node],
+        "reshape_constant_zero_copy_test",
+        inputs=[],
+        initializer=[
+            numpy_helper.from_array(data, "data"),
+            helper.make_tensor("shape", TensorProto.INT64, [2], [0, 3]),
+        ],
+        outputs=[helper.make_tensor_value_info("reshaped", TensorProto.FLOAT, [2, 3])],
+    )
+    model = helper.make_model(
+        graph,
+        producer_name="reshape_constant_zero_copy_test",
+        opset_imports=[helper.make_opsetid("", 14)],
+    )
+
+    output = run_in_tvm(model, opset=14)
+
+    tvm.testing.assert_allclose(output.numpy(), data)
+
+
+def test_reshape_allowzero_literal_zero_dimension():
+    reshape_node = helper.make_node("Reshape", ["data", "shape"], ["reshaped"], allowzero=1)
+    graph = helper.make_graph(
+        [reshape_node],
+        "reshape_allowzero_test",
+        inputs=[helper.make_tensor_value_info("data", TensorProto.FLOAT, [2, 0])],
+        initializer=[helper.make_tensor("shape", TensorProto.INT64, [2], [0, 2])],
+        outputs=[helper.make_tensor_value_info("reshaped", TensorProto.FLOAT, [0, 2])],
+    )
+    model = helper.make_model(
+        graph,
+        producer_name="reshape_allowzero_test",
+        opset_imports=[helper.make_opsetid("", 14)],
+    )
+
+    output = run_in_tvm(model, {"data": np.zeros((2, 0), dtype="float32")}, opset=14)
+
+    assert tuple(output.shape) == (0, 2)
+
+
+def test_reshape_allowzero_infers_dimension_without_literal_zero():
+    reshape_node = helper.make_node("Reshape", ["data", "shape"], ["reshaped"], allowzero=1)
+    graph = helper.make_graph(
+        [reshape_node],
+        "reshape_allowzero_infer_dimension_test",
+        inputs=[helper.make_tensor_value_info("data", TensorProto.FLOAT, [3, 4])],
+        initializer=[helper.make_tensor("shape", TensorProto.INT64, [2], [-1, 2])],
+        outputs=[helper.make_tensor_value_info("reshaped", TensorProto.FLOAT, [6, 2])],
+    )
+    model = helper.make_model(
+        graph,
+        producer_name="reshape_allowzero_infer_dimension_test",
+        opset_imports=[helper.make_opsetid("", 14)],
+    )
+    data = np.arange(12, dtype="float32").reshape(3, 4)
+
+    output = run_in_tvm(model, {"data": data}, opset=14)
+
+    tvm.testing.assert_allclose(output.numpy(), data.reshape(6, 2))
+
+
 def test_reshape_shape_output():
     def verify_reshape_shape_output(target_shape, output_shape, expected):
         shape_node = helper.make_node("Shape", ["data"], ["shape_out"])