diff --git a/backends/webgpu/test/ops/test_to_copy.py b/backends/webgpu/test/ops/test_to_copy.py
new file mode 100644
index 00000000000..baf068a3438
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+++ b/backends/webgpu/test/ops/test_to_copy.py
@@ -0,0 +1,65 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""`aten._to_copy.default` modules for the WebGPU op-test framework.
+
+`ToCopyIntToFloatModule` / `ToCopyFloatModule` drive the export-delegation smoke
+test (mirroring `test_view_copy.py`). The int32 -> fp32 numeric convert — input
+int `[1, 2, 3]` -> float `[1.0, 2.0, 3.0]`, NOT the byte-reinterpretation
+`0x1 -> 1.4e-45` — and the fp32 same-dtype passthrough are value-checked by the
+lvp golden (yolo11n / Depth-Anything-V2).
+"""
+
+import unittest
+
+import torch
+
+from executorch.backends.vulkan.partitioner.vulkan_partitioner import VulkanPartitioner
+from executorch.exir import to_edge_transform_and_lower
+
+
+class ToCopyIntToFloatModule(torch.nn.Module):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # int32 -> fp32 dtype cast (the numeric-convert path).
+        return x.to(torch.float32)
+
+
+class ToCopyFloatModule(torch.nn.Module):
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        # Same-dtype copy (flat byte-copy path); copy=True keeps the op from
+        # being elided as a no-op.
+        return x.to(torch.float32, copy=True)
+
+
+def _export(model: torch.nn.Module, x: torch.Tensor):
+    ep = torch.export.export(model.eval(), (x,))
+    return to_edge_transform_and_lower(
+        ep, partitioner=[VulkanPartitioner()]
+    ).to_executorch()
+
+
+def _delegated(et) -> bool:
+    return any(
+        d.id == "VulkanBackend"
+        for plan in et.executorch_program.execution_plan
+        for d in plan.delegates
+    )
+
+
+class ToCopyTest(unittest.TestCase):
+    def test_int_to_float_delegates(self) -> None:
+        x = torch.tensor([1, 2, 3], dtype=torch.int32)
+        et = _export(ToCopyIntToFloatModule(), x)
+        self.assertTrue(
+            _delegated(et), "Expected a VulkanBackend delegate (to_copy int->float)"
+        )
+
+    def test_float_passthrough_delegates(self) -> None:
+        x = torch.tensor([1.0, 2.0, 3.0], dtype=torch.float32)
+        et = _export(ToCopyFloatModule(), x)
+        self.assertTrue(
+            _delegated(et), "Expected a VulkanBackend delegate (to_copy float->float)"
+        )