diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
index 73e74c228ba..e6a73fdbb59 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
@@ -44,8 +44,7 @@ class LiteLLMMessagesToCompletionTransformationHandler:
 
         For OpenAI models, Chat Completions typically does not return reasoning text
         (only token accounting). To return a thinking-like content block in the
-        Anthropic response format, we route the request through OpenAI's Responses API
-        and request a reasoning summary.
+        Anthropic response format, we route the request through OpenAI's Responses API.
         """
         custom_llm_provider = completion_kwargs.get("custom_llm_provider")
         if custom_llm_provider is None:
@@ -80,16 +79,7 @@ class LiteLLMMessagesToCompletionTransformationHandler:
         if isinstance(reasoning_effort, str) and reasoning_effort:
             completion_kwargs["reasoning_effort"] = {
                 "effort": reasoning_effort,
-                "summary": "detailed",
             }
-        elif isinstance(reasoning_effort, dict):
-            if (
-                "summary" not in reasoning_effort
-                and "generate_summary" not in reasoning_effort
-            ):
-                updated_reasoning_effort = dict(reasoning_effort)
-                updated_reasoning_effort["summary"] = "detailed"
-                completion_kwargs["reasoning_effort"] = updated_reasoning_effort
 
     @staticmethod
     def _prepare_completion_kwargs(
diff --git a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
index 376d14416a3..a4d5ade568b 100644
--- a/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
+++ b/tests/test_litellm/llms/anthropic/experimental_pass_through/messages/test_anthropic_experimental_pass_through_messages_handler.py
@@ -177,8 +177,8 @@ def test_openai_model_with_thinking_converts_to_reasoning_effort():
         # Verify reasoning_effort is set (converted from thinking)
         assert "reasoning_effort" in call_kwargs, "reasoning_effort should be passed to completion"
 
-        # reasoning_effort is transformed into a dict with effort and summary fields
-        expected_reasoning_effort = {"effort": "minimal", "summary": "detailed"}
+        # reasoning_effort is transformed into a dict with effort field only (no hardcoded summary)
+        expected_reasoning_effort = {"effort": "minimal"}
         assert call_kwargs["reasoning_effort"] == expected_reasoning_effort, \
             f"reasoning_effort should be {expected_reasoning_effort} for budget_tokens=1024, got {call_kwargs.get('reasoning_effort')}"
 
@@ -218,3 +218,38 @@ class TestThinkingParameterTransformation:
         
         assert result == {"reasoning_effort": "minimal"}
         assert "thinking" not in result
+
+
+class TestNoHardcodedReasoningSummary:
+    """Tests for issue #20998: adapter must not hardcode reasoning summary.
+
+    Per OpenAI spec, reasoning.summary is opt-in. The adapter should not
+    inject summary='detailed' when the user didn't request it.
+    """
+
+    def test_no_summary_added_when_not_requested(self):
+        """reasoning_effort dict should only contain 'effort', no 'summary'."""
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        thinking = {"type": "enabled", "budget_tokens": 5000}
+        completion_kwargs = {"model": "openai/gpt-5.1", "reasoning_effort": "medium"}
+        LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+            completion_kwargs, thinking=thinking
+        )
+        assert completion_kwargs["reasoning_effort"] == {"effort": "medium"}
+        assert "summary" not in completion_kwargs["reasoning_effort"]
+
+    def test_model_prefixed_with_responses(self):
+        """Model should be prefixed with 'responses/' for Responses API routing."""
+        from litellm.llms.anthropic.experimental_pass_through.adapters.handler import (
+            LiteLLMMessagesToCompletionTransformationHandler,
+        )
+
+        thinking = {"type": "enabled", "budget_tokens": 5000}
+        completion_kwargs = {"model": "openai/gpt-5.1", "reasoning_effort": "medium"}
+        LiteLLMMessagesToCompletionTransformationHandler._route_openai_thinking_to_responses_api_if_needed(
+            completion_kwargs, thinking=thinking
+        )
+        assert completion_kwargs["model"] == "responses/openai/gpt-5.1"