diff --git a/tests/v1/entrypoints/llm/test_struct_output_generate.py b/tests/v1/entrypoints/llm/test_struct_output_generate.py
index 6bdfa0fae4a2..00fa47575b6a 100644
--- a/tests/v1/entrypoints/llm/test_struct_output_generate.py
+++ b/tests/v1/entrypoints/llm/test_struct_output_generate.py
@@ -4,10 +4,12 @@
 
 import json
 import re
+from enum import Enum
 from typing import Any
 
 import jsonschema
 import pytest
+from pydantic import BaseModel
 
 from vllm.entrypoints.llm import LLM
 from vllm.outputs import RequestOutput
@@ -390,3 +392,54 @@ def test_guided_choice_completion(
         assert generated_text is not None
         assert generated_text in sample_guided_choice
         print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+
+
+class CarType(str, Enum):
+    sedan = "sedan"
+    suv = "SUV"
+    truck = "Truck"
+    coupe = "Coupe"
+
+
+class CarDescription(BaseModel):
+    brand: str
+    model: str
+    car_type: CarType
+
+
+@pytest.mark.skip_global_cleanup
+@pytest.mark.parametrize("guided_decoding_backend",
+                         GUIDED_DECODING_BACKENDS_V1)
+@pytest.mark.parametrize("model_name", MODELS_TO_TEST)
+def test_guided_json_completion_with_enum(
+    monkeypatch: pytest.MonkeyPatch,
+    guided_decoding_backend: str,
+    model_name: str,
+):
+    monkeypatch.setenv("VLLM_USE_V1", "1")
+    llm = LLM(model=model_name,
+              max_model_len=1024,
+              guided_decoding_backend=guided_decoding_backend)
+    json_schema = CarDescription.model_json_schema()
+    sampling_params = SamplingParams(
+        temperature=1.0,
+        max_tokens=1000,
+        guided_decoding=GuidedDecodingParams(json=json_schema))
+    outputs = llm.generate(
+        prompts="Generate a JSON with the brand, model and car_type of"
+        "the most iconic car from the 90's",
+        sampling_params=sampling_params,
+        use_tqdm=True)
+
+    assert outputs is not None
+
+    for output in outputs:
+        assert output is not None
+        assert isinstance(output, RequestOutput)
+        prompt = output.prompt
+
+        generated_text = output.outputs[0].text
+        assert generated_text is not None
+        print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
+        output_json = json.loads(generated_text)
+        jsonschema.validate(instance=output_json, schema=json_schema)
diff --git a/vllm/v1/structured_output/utils.py b/vllm/v1/structured_output/utils.py
index 694e46f763f0..a771256ef29f 100644
--- a/vllm/v1/structured_output/utils.py
+++ b/vllm/v1/structured_output/utils.py
@@ -26,10 +26,6 @@ def check_object(obj: dict[str, Any]) -> bool:
         if "pattern" in obj:
             return True
 
-        # Check for enum restrictions
-        if "enum" in obj:
-            return True
-
         # Check for numeric ranges
         if obj.get("type") in ("integer", "number") and any(
                 key in obj