diff --git a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
index e6a73fdbb59..73e74c228ba 100644
--- a/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
+++ b/litellm/llms/anthropic/experimental_pass_through/adapters/handler.py
@@ -44,7 +44,8 @@ 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.
+        Anthropic response format, we route the request through OpenAI's Responses API
+        and request a reasoning summary.
         """
         custom_llm_provider = completion_kwargs.get("custom_llm_provider")
         if custom_llm_provider is None:
@@ -79,7 +80,16 @@ 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/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py
index 05d14704905..935babe4380 100644
--- a/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py
+++ b/litellm/llms/anthropic/experimental_pass_through/responses_adapters/transformation.py
@@ -241,7 +241,7 @@ class LiteLLMAnthropicToResponsesAPIAdapter:
             effort = "low"
         else:
             effort = "minimal"
-        return {"effort": effort}
+        return {"effort": effort, "summary": "detailed"}
 
     def translate_request(
         self,
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 24c3f24bc79..636e84fe796 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
@@ -180,8 +180,7 @@ def test_openai_model_with_thinking_converts_to_reasoning():
         assert "reasoning" in call_kwargs, "reasoning should be passed to litellm.responses"
 
         # budget_tokens=1024 -> effort="minimal" (< 2000 threshold)
-        # summary should NOT be hardcoded — it's opt-in per the OpenAI spec
-        expected_reasoning = {"effort": "minimal"}
+        expected_reasoning = {"effort": "minimal", "summary": "detailed"}
         assert call_kwargs["reasoning"] == expected_reasoning, (
             f"reasoning should be {expected_reasoning} for budget_tokens=1024, "
             f"got {call_kwargs.get('reasoning')}"
@@ -223,38 +222,3 @@ 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"