diff --git a/tests/generation/test_utils.py b/tests/generation/test_utils.py
index 34617810cfc0..682cd9e171f7 100644
--- a/tests/generation/test_utils.py
+++ b/tests/generation/test_utils.py
@@ -2261,17 +2261,31 @@ def test_forward_with_logits_to_keep(self):
             torch.testing.assert_close(all_logits[:, -1:, :], last_token_logits, rtol=1e-5, atol=1e-5)
 
     def test_generate_with_and_without_position_ids(self):
+        ran_any_model = False
         for model_class in self.all_generative_model_classes:
             config, inputs_dict = self.prepare_config_and_inputs_for_generate()
             model = model_class(config).to(torch_device).eval()
             model_forward_args = inspect.signature(model.forward).parameters
 
+            has_3d_rope_positions = any(
+                hasattr(module, "get_rope_index")
+                for module in (
+                    model,
+                    getattr(model, "model", None),
+                    getattr(model, "language_model", None),
+                    getattr(model, "text_model", None),
+                )
+            )
+            if has_3d_rope_positions:
+                continue
+
             if "position_ids" not in model_forward_args or "input_ids" not in inputs_dict:
                 self.skipTest("This model doesn't use `position_ids`")
 
             if config.is_encoder_decoder:
                 self.skipTest("This model doesn't prepare `position_ids` in generate")
 
+            ran_any_model = True
             input_ids = inputs_dict["input_ids"]
             seq_length = input_ids.shape[1]
             # ensure left padding
@@ -2304,6 +2318,12 @@ def test_generate_with_and_without_position_ids(self):
             # and can continue adding new ids to the already passed position ids
             self.assertListEqual(out_wo_positions.tolist(), out_w_positions.tolist())
 
+        if not ran_any_model:
+            self.skipTest(
+                "All model classes in this test use 3D RoPE positions (`get_rope_index`), for which 2D custom "
+                "`position_ids` may be accepted but are expected to produce invalid outputs."
+            )
+
     def _check_generate_outputs(self, output, config, use_cache=False, num_return_sequences=1, num_beams=1):
         input_batch_size = int(output.sequences.shape[0] / num_return_sequences)
         internal_batch_size = (