diff --git a/tests/test_litellm/responses/test_responses_utils.py b/tests/test_litellm/responses/test_responses_utils.py
index 7feab9c6035..c6f32b6d758 100644
--- a/tests/test_litellm/responses/test_responses_utils.py
+++ b/tests/test_litellm/responses/test_responses_utils.py
@@ -384,3 +384,35 @@ def test_responses_extra_body_forwarded_to_completion_transformation_handler():
         assert call_kwargs.kwargs.get("extra_body") == {
             "custom_key": "custom_value"
         }
+
+
+def test_responses_maps_reasoning_effort_from_litellm_params_to_reasoning():
+    """
+    Test that when reasoning_effort is passed in kwargs (e.g. from proxy litellm_params)
+    and reasoning is None, it is mapped to reasoning before the request.
+
+    Supports per-model reasoning_effort/summary config in proxy for clients like Open WebUI
+    that cannot set extra_body.
+    """
+    with patch(
+        "litellm.responses.main.ProviderConfigManager.get_provider_responses_api_config",
+        return_value=None,
+    ), patch(
+        "litellm.responses.main.litellm_completion_transformation_handler.response_api_handler",
+    ) as mock_handler:
+        mock_handler.return_value = MagicMock()
+
+        litellm.responses(
+            model="openai/gpt-4o",
+            input="Hello",
+            reasoning_effort={"effort": "high", "summary": "detailed"},
+        )
+
+        mock_handler.assert_called_once()
+        call_kwargs = mock_handler.call_args
+        responses_api_request = call_kwargs.kwargs.get("responses_api_request", {})
+        assert "reasoning" in responses_api_request
+        assert responses_api_request["reasoning"] == {
+            "effort": "high",
+            "summary": "detailed",
+        }
diff --git a/tests/test_litellm/test_router.py b/tests/test_litellm/test_router.py
index 9dcb16b545e..c55b26ca39c 100644
--- a/tests/test_litellm/test_router.py
+++ b/tests/test_litellm/test_router.py
@@ -1877,19 +1877,20 @@ async def test_anthropic_messages_call_type_is_cached():
     in PromptCachingDeploymentCheck.async_log_success_event.
     """
     import asyncio
+
+    from litellm.caching.dual_cache import DualCache
     from litellm.router_utils.pre_call_checks.prompt_caching_deployment_check import (
         PromptCachingDeploymentCheck,
     )
     from litellm.router_utils.prompt_caching_cache import PromptCachingCache
-    from litellm.caching.dual_cache import DualCache
-    from litellm.types.utils import CallTypes
     from litellm.types.utils import (
-        StandardLoggingPayload,
-        StandardLoggingModelInformation,
-        StandardLoggingMetadata,
+        CallTypes,
         StandardLoggingHiddenParams,
+        StandardLoggingMetadata,
+        StandardLoggingModelInformation,
+        StandardLoggingPayload,
     )
-    
+
     # Create mock standard logging payload inline
     def create_standard_logging_payload() -> StandardLoggingPayload:
         return StandardLoggingPayload(
@@ -2081,3 +2082,107 @@ def test_update_kwargs_with_deployment_no_tags():
 
     # No tags key should be added if deployment has no tags
     assert "tags" not in kwargs["metadata"]
+
+
+def test_update_kwargs_with_deployment_merges_tools():
+    """
+    Test that when both deployment litellm_params and request have tools,
+    they are merged (deployment tools first, then request tools).
+
+    Supports proxy-configured tools (e.g. for o3 deep research) merged with
+    client-provided tools.
+    """
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "o3-deep-research",
+                "litellm_params": {
+                    "model": "openai/o3-deep-research",
+                    "api_key": "fake-key",
+                    "tools": [{"type": "web_search"}],
+                    "tool_choice": "auto",
+                },
+            },
+        ],
+    )
+
+    kwargs: dict = {
+        "metadata": {},
+        "tools": [
+            {
+                "type": "function",
+                "function": {"name": "get_weather", "description": "Get weather"},
+            },
+        ],
+    }
+    deployment = router.get_deployment_by_model_group_name(
+        model_group_name="o3-deep-research"
+    )
+    router._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
+
+    # Tools should be merged: deployment first, then request
+    assert "tools" in kwargs
+    assert len(kwargs["tools"]) == 2
+    assert kwargs["tools"][0] == {"type": "web_search"}
+    assert kwargs["tools"][1]["function"]["name"] == "get_weather"
+    # tool_choice from request (none) - deployment's should be used
+    assert kwargs["tool_choice"] == "auto"
+
+
+def test_update_kwargs_with_deployment_merge_tools_deployment_only():
+    """
+    Test that when only deployment has tools, they are applied to kwargs.
+    """
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "o3-deep-research",
+                "litellm_params": {
+                    "model": "openai/o3-deep-research",
+                    "api_key": "fake-key",
+                    "tools": [{"type": "web_search"}],
+                    "tool_choice": "required",
+                },
+            },
+        ],
+    )
+
+    kwargs: dict = {"metadata": {}}
+    deployment = router.get_deployment_by_model_group_name(
+        model_group_name="o3-deep-research"
+    )
+    router._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
+
+    assert kwargs["tools"] == [{"type": "web_search"}]
+    assert kwargs["tool_choice"] == "required"
+
+
+def test_update_kwargs_with_deployment_merge_tools_request_overrides_tool_choice():
+    """
+    Test that when request has tool_choice, it overrides deployment's.
+    """
+    router = litellm.Router(
+        model_list=[
+            {
+                "model_name": "o3-deep-research",
+                "litellm_params": {
+                    "model": "openai/o3-deep-research",
+                    "api_key": "fake-key",
+                    "tools": [{"type": "web_search"}],
+                    "tool_choice": "auto",
+                },
+            },
+        ],
+    )
+
+    kwargs: dict = {
+        "metadata": {},
+        "tool_choice": "none",
+    }
+    deployment = router.get_deployment_by_model_group_name(
+        model_group_name="o3-deep-research"
+    )
+    router._update_kwargs_with_deployment(deployment=deployment, kwargs=kwargs)
+
+    # Request tool_choice should be preserved (merged tools still applied)
+    assert kwargs["tool_choice"] == "none"