diff --git a/examples/online_serving/openai_chat_completion_structured_outputs.py b/examples/online_serving/openai_chat_completion_structured_outputs.py
index 9c57af1c1..660369e55 100644
--- a/examples/online_serving/openai_chat_completion_structured_outputs.py
+++ b/examples/online_serving/openai_chat_completion_structured_outputs.py
@@ -138,7 +138,7 @@ def main():
         api_key="-",
     )
 
-    model = "Qwen/Qwen2.5-3B-Instruct"
+    model = client.models.list().data[0].id
 
     print("Guided Choice Completion:")
     print(guided_choice_completion(client, model))
diff --git a/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py b/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py
index b807bc540..42aa12c45 100644
--- a/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py
+++ b/examples/online_serving/openai_chat_completion_structured_outputs_structural_tag.py
@@ -59,7 +59,7 @@ and San Francisco?
     }]
 
     response = client.chat.completions.create(
-        model="meta-llama/Llama-3.1-8B-Instruct",
+        model=client.models.list().data[0].id,
         messages=messages,
         response_format={
             "type":
diff --git a/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py b/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
index 5da9236c5..a04f0cdf1 100644
--- a/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
+++ b/examples/online_serving/openai_chat_completion_structured_outputs_with_reasoning.py
@@ -4,7 +4,7 @@ An example shows how to generate structured outputs from reasoning models
 like DeepSeekR1. The thinking process will not be guided by the JSON
 schema provided by the user. Only the final output will be structured.
 
-To run this example, you need to start the vLLM server with the reasoning 
+To run this example, you need to start the vLLM server with the reasoning
 parser:
 
 ```bash