diff --git a/tests/full_tests/structured_outputs.py b/tests/full_tests/structured_outputs.py
index d4977b2e..e696a70b 100644
--- a/tests/full_tests/structured_outputs.py
+++ b/tests/full_tests/structured_outputs.py
@@ -2,11 +2,10 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 # Copied from vllm/examples/offline_inference/
 """
-This file demonstrates the example usage of guided decoding
-to generate structured outputs using vLLM. It shows how to apply
-different guided decoding techniques such as Choice, Regex, JSON schema,
-and Grammar to produce structured and formatted results
-based on specific prompts.
+This file demonstrates the example usage of structured outputs
+in vLLM. It shows how to apply different constraints such as choice,
+regex, json schema, and grammar to produce structured and formatted
+results based on specific prompts.
 """
 
 from enum import Enum
@@ -14,22 +13,28 @@
 from pydantic import BaseModel
 
 from vllm import LLM, SamplingParams
-from vllm.sampling_params import GuidedDecodingParams
+from vllm.sampling_params import StructuredOutputsParams
 
-# Guided decoding by Choice (list of possible options)
-guided_decoding_params_choice = GuidedDecodingParams(choice=["Positive", "Negative"])
-sampling_params_choice = SamplingParams(guided_decoding=guided_decoding_params_choice)
+MAX_TOKENS = 1024
+
+# Structured outputs by Choice (list of possible options)
+structured_outputs_params_choice = StructuredOutputsParams(choice=["Positive", "Negative"])
+sampling_params_choice = SamplingParams(structured_outputs=structured_outputs_params_choice)
 prompt_choice = "Classify this sentiment: vLLM is wonderful!"
 
-# Guided decoding by Regex
-guided_decoding_params_regex = GuidedDecodingParams(regex=r"\w+@\w+\.com\n")
-sampling_params_regex = SamplingParams(guided_decoding=guided_decoding_params_regex, stop=["\n"])
+# Structured outputs by Regex
+structured_outputs_params_regex = StructuredOutputsParams(regex=r"\w+@\w+\.com\n")
+sampling_params_regex = SamplingParams(
+    structured_outputs=structured_outputs_params_regex,
+    stop=["\n"],
+    max_tokens=MAX_TOKENS,
+)
 prompt_regex = ("Generate an email address for Alan Turing, who works in Enigma."
                 "End in .com and new line. Example result:"
                 "alan.turing@enigma.com\n")
 
 
-# Guided decoding by JSON using Pydantic schema
+# Structured outputs by JSON using Pydantic schema
 class CarType(str, Enum):
     sedan = "sedan"
     suv = "SUV"
@@ -44,12 +49,12 @@ class CarDescription(BaseModel):
 
 
 json_schema = CarDescription.model_json_schema()
-guided_decoding_params_json = GuidedDecodingParams(json=json_schema)
-sampling_params_json = SamplingParams(guided_decoding=guided_decoding_params_json)
-prompt_json = ("Generate a JSON with the brand, model and car_type of"
+structured_outputs_params_json = StructuredOutputsParams(json=json_schema)
+sampling_params_json = SamplingParams(structured_outputs=structured_outputs_params_json, max_tokens=MAX_TOKENS)
+prompt_json = ("Generate a JSON with the brand, model and car_type of "
                "the most iconic car from the 90's")
 
-# Guided decoding by Grammar
+# Structured outputs by Grammar
 simplified_sql_grammar = """
 root ::= select_statement
 select_statement ::= "SELECT " column " from " table " where " condition
@@ -58,10 +63,9 @@ class CarDescription(BaseModel):
 condition ::= column "= " number
 number ::= "1 " | "2 "
 """
-guided_decoding_params_grammar = GuidedDecodingParams(grammar=simplified_sql_grammar)
-sampling_params_grammar = SamplingParams(guided_decoding=guided_decoding_params_grammar)
-prompt_grammar = ("Generate an SQL query to show the "
-                  "'username' and 'email'from the 'users' table.")
+structured_outputs_params_grammar = StructuredOutputsParams(grammar=simplified_sql_grammar)
+sampling_params_grammar = SamplingParams(structured_outputs=structured_outputs_params_grammar, max_tokens=MAX_TOKENS)
+prompt_grammar = ("Generate an SQL query to show the 'username' and 'email' from the 'users' table.")
 
 
 def format_output(title: str, output: str):
@@ -77,16 +81,16 @@ def main():
     llm = LLM(model="Qwen/Qwen2.5-3B-Instruct", max_model_len=256)
 
     choice_output = generate_output(prompt_choice, sampling_params_choice, llm)
-    format_output("Guided decoding by Choice", choice_output)
+    format_output("Structured outputs by Choice", choice_output)
 
     regex_output = generate_output(prompt_regex, sampling_params_regex, llm)
-    format_output("Guided decoding by Regex", regex_output)
+    format_output("Structured outputs by Regex", regex_output)
 
     json_output = generate_output(prompt_json, sampling_params_json, llm)
-    format_output("Guided decoding by JSON", json_output)
+    format_output("Structured outputs by JSON", json_output)
 
     grammar_output = generate_output(prompt_grammar, sampling_params_grammar, llm)
-    format_output("Guided decoding by Grammar", grammar_output)
+    format_output("Structured outputs by Grammar", grammar_output)
 
 
 if __name__ == "__main__":