[Frontend] Fix reasoning_tokens for text-based parsers in Responses API (#33513)

Signed-off-by: Jaeyeon Kim <anencore94@gmail.com>

tests/entrypoints/openai/test_serving_responses.py

@@ -13,9 +13,13 @@ from openai.types.responses.tool import (
     Tool,
 )
 
+import vllm.envs as envs
 from vllm.entrypoints.mcp.tool_server import ToolServer
-from vllm.entrypoints.openai.engine.protocol import ErrorResponse
-from vllm.entrypoints.openai.responses.context import ConversationContext
+from vllm.entrypoints.openai.engine.protocol import (
+    ErrorResponse,
+    RequestResponseMetadata,
+)
+from vllm.entrypoints.openai.responses.context import ConversationContext, SimpleContext
 from vllm.entrypoints.openai.responses.protocol import ResponsesRequest
 from vllm.entrypoints.openai.responses.serving import (
     OpenAIServingResponses,
@@ -23,6 +27,8 @@ from vllm.entrypoints.openai.responses.serving import (
     extract_tool_types,
 )
 from vllm.inputs.data import TokensPrompt
+from vllm.outputs import CompletionOutput, RequestOutput
+from vllm.sampling_params import SamplingParams
 
 
 class MockConversationContext(ConversationContext):
@@ -259,6 +265,87 @@ class TestValidateGeneratorInput:
         assert isinstance(result, ErrorResponse)
 
 
+@pytest.mark.asyncio
+async def test_reasoning_tokens_counted_for_text_reasoning_model(monkeypatch):
+    """Ensure reasoning_tokens usage is derived from thinking token spans."""
+
+    class FakeTokenizer:
+        def __init__(self):
+            self._vocab = {"<think>": 1, "</think>": 2, "reason": 3, "final": 4}
+
+        def get_vocab(self):
+            return self._vocab
+
+    # Force non-harmony, SimpleContext path
+    monkeypatch.setattr(envs, "VLLM_USE_EXPERIMENTAL_PARSER_CONTEXT", False)
+
+    engine_client = MagicMock()
+    model_config = MagicMock()
+    model_config.hf_config.model_type = "test"
+    model_config.hf_text_config = MagicMock()
+    model_config.get_diff_sampling_param.return_value = {}
+    engine_client.model_config = model_config
+    engine_client.input_processor = MagicMock()
+    engine_client.io_processor = MagicMock()
+    engine_client.renderer = MagicMock()
+
+    tokenizer = FakeTokenizer()
+    engine_client.renderer.get_tokenizer.return_value = tokenizer
+
+    models = MagicMock()
+
+    serving = OpenAIServingResponses(
+        engine_client=engine_client,
+        models=models,
+        request_logger=None,
+        chat_template=None,
+        chat_template_content_format="auto",
+        reasoning_parser="qwen3",
+    )
+
+    # Build a SimpleContext with thinking tokens in the output.
+    context = SimpleContext()
+    token_ids = [1, 10, 2, 20]  # <think> 10 </think> 20 -> reasoning token count = 1
+    completion = CompletionOutput(
+        index=0,
+        text="<think>reason</think>final",
+        token_ids=token_ids,
+        cumulative_logprob=0.0,
+        logprobs=None,
+        finish_reason="stop",
+        stop_reason=None,
+    )
+    req_output = RequestOutput(
+        request_id="req",
+        prompt="hi",
+        prompt_token_ids=[7, 8],
+        prompt_logprobs=None,
+        outputs=[completion],
+        finished=True,
+        num_cached_tokens=0,
+    )
+    context.append_output(req_output)
+
+    async def dummy_result_generator():
+        yield None
+
+    request = ResponsesRequest(input="hi", tools=[], stream=False)
+    sampling_params = SamplingParams(max_tokens=16)
+    metadata = RequestResponseMetadata(request_id="req")
+
+    response = await serving.responses_full_generator(
+        request=request,
+        sampling_params=sampling_params,
+        result_generator=dummy_result_generator(),
+        context=context,
+        model_name="test-model",
+        tokenizer=tokenizer,
+        request_metadata=metadata,
+    )
+
+    assert response.usage.output_tokens_details.reasoning_tokens == 1
+
+
 class TestExtractAllowedToolsFromMcpRequests:
     """Test class for _extract_allowed_tools_from_mcp_requests function"""