diff --git a/docs/ref/realtime/config.md b/docs/ref/realtime/config.md
index 3e50f47ad..2445c6a34 100644
--- a/docs/ref/realtime/config.md
+++ b/docs/ref/realtime/config.md
@@ -11,6 +11,7 @@
 ## Audio Configuration
 
 ::: agents.realtime.config.RealtimeInputAudioTranscriptionConfig
+::: agents.realtime.config.RealtimeInputAudioNoiseReductionConfig
 ::: agents.realtime.config.RealtimeTurnDetectionConfig
 
 ## Guardrails Settings
diff --git a/src/agents/extensions/models/litellm_model.py b/src/agents/extensions/models/litellm_model.py
index 4369b342b..8d39ad390 100644
--- a/src/agents/extensions/models/litellm_model.py
+++ b/src/agents/extensions/models/litellm_model.py
@@ -257,7 +257,15 @@ async def _fetch_response(
         stream: bool = False,
         prompt: Any | None = None,
     ) -> litellm.types.utils.ModelResponse | tuple[Response, AsyncStream[ChatCompletionChunk]]:
-        converted_messages = Converter.items_to_messages(input)
+        # Preserve reasoning messages for tool calls when reasoning is on
+        # This is needed for models like Claude 4 Sonnet/Opus which support interleaved thinking
+        preserve_thinking_blocks = (
+            model_settings.reasoning is not None and model_settings.reasoning.effort is not None
+        )
+
+        converted_messages = Converter.items_to_messages(
+            input, preserve_thinking_blocks=preserve_thinking_blocks
+        )
 
         if system_instructions:
             converted_messages.insert(
diff --git a/src/agents/models/chatcmpl_converter.py b/src/agents/models/chatcmpl_converter.py
index 77ff22ee0..96f02a5fe 100644
--- a/src/agents/models/chatcmpl_converter.py
+++ b/src/agents/models/chatcmpl_converter.py
@@ -39,7 +39,7 @@
     ResponseReasoningItemParam,
 )
 from openai.types.responses.response_input_param import FunctionCallOutput, ItemReference, Message
-from openai.types.responses.response_reasoning_item import Summary
+from openai.types.responses.response_reasoning_item import Content, Summary
 
 from ..agent_output import AgentOutputSchemaBase
 from ..exceptions import AgentsException, UserError
@@ -93,7 +93,9 @@ def convert_response_format(
     def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TResponseOutputItem]:
         items: list[TResponseOutputItem] = []
 
-        # Handle reasoning content if available
+        # Check if message is agents.extentions.models.litellm_model.InternalChatCompletionMessage
+        # We can't actually import it here because litellm is an optional dependency
+        # So we use hasattr to check for reasoning_content and thinking_blocks
         if hasattr(message, "reasoning_content") and message.reasoning_content:
             reasoning_item = ResponseReasoningItem(
                 id=FAKE_RESPONSES_ID,
@@ -101,16 +103,28 @@ def message_to_output_items(cls, message: ChatCompletionMessage) -> list[TRespon
                 type="reasoning",
             )
 
-            # Store full thinking blocks for Anthropic compatibility
+            # Store thinking blocks for Anthropic compatibility
             if hasattr(message, "thinking_blocks") and message.thinking_blocks:
-                # Store thinking blocks in the reasoning item's content
-                # Convert thinking blocks to Content objects
-                from openai.types.responses.response_reasoning_item import Content
-
-                reasoning_item.content = [
-                    Content(text=str(block.get("thinking", "")), type="reasoning_text")
-                    for block in message.thinking_blocks
-                ]
+                # Store thinking text in content and signature in encrypted_content
+                reasoning_item.content = []
+                signature = None
+                for block in message.thinking_blocks:
+                    if isinstance(block, dict):
+                        thinking_text = block.get("thinking", "")
+                        if thinking_text:
+                            reasoning_item.content.append(
+                                Content(text=thinking_text, type="reasoning_text")
+                            )
+                        # Store the signature if present
+                        if block.get("signature"):
+                            signature = block.get("signature")
+
+                # Store only the last signature in encrypted_content
+                # If there are multiple thinking blocks, this should be a problem.
+                # In practice, there should only be one signature for the entire reasoning step.
+                # Tested with: claude-sonnet-4-20250514
+                if signature:
+                    reasoning_item.encrypted_content = signature
 
             items.append(reasoning_item)
 
@@ -301,10 +315,18 @@ def extract_all_content(
     def items_to_messages(
         cls,
         items: str | Iterable[TResponseInputItem],
+        preserve_thinking_blocks: bool = False,
     ) -> list[ChatCompletionMessageParam]:
         """
         Convert a sequence of 'Item' objects into a list of ChatCompletionMessageParam.
 
+        Args:
+            items: A string or iterable of response input items to convert
+            preserve_thinking_blocks: Whether to preserve thinking blocks in tool calls
+                for reasoning models like Claude 4 Sonnet/Opus which support interleaved
+                thinking. When True, thinking blocks are reconstructed and included in
+                assistant messages with tool calls.
+
         Rules:
         - EasyInputMessage or InputMessage (role=user) => ChatCompletionUserMessageParam
         - EasyInputMessage or InputMessage (role=system) => ChatCompletionSystemMessageParam
@@ -325,6 +347,7 @@ def items_to_messages(
 
         result: list[ChatCompletionMessageParam] = []
         current_assistant_msg: ChatCompletionAssistantMessageParam | None = None
+        pending_thinking_blocks: list[dict[str, str]] | None = None
 
         def flush_assistant_message() -> None:
             nonlocal current_assistant_msg
@@ -336,10 +359,11 @@ def flush_assistant_message() -> None:
                 current_assistant_msg = None
 
         def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
-            nonlocal current_assistant_msg
+            nonlocal current_assistant_msg, pending_thinking_blocks
             if current_assistant_msg is None:
                 current_assistant_msg = ChatCompletionAssistantMessageParam(role="assistant")
                 current_assistant_msg["tool_calls"] = []
+
             return current_assistant_msg
 
         for item in items:
@@ -455,6 +479,13 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
 
             elif func_call := cls.maybe_function_tool_call(item):
                 asst = ensure_assistant_message()
+
+                # If we have pending thinking blocks, use them as the content
+                # This is required for Anthropic API tool calls with interleaved thinking
+                if pending_thinking_blocks:
+                    asst["content"] = pending_thinking_blocks  # type: ignore
+                    pending_thinking_blocks = None  # Clear after using
+
                 tool_calls = list(asst.get("tool_calls", []))
                 arguments = func_call["arguments"] if func_call["arguments"] else "{}"
                 new_tool_call = ChatCompletionMessageFunctionToolCallParam(
@@ -483,9 +514,28 @@ def ensure_assistant_message() -> ChatCompletionAssistantMessageParam:
                     f"Encountered an item_reference, which is not supported: {item_ref}"
                 )
 
-            # 7) reasoning message => not handled
-            elif cls.maybe_reasoning_message(item):
-                pass
+            # 7) reasoning message => extract thinking blocks if present
+            elif reasoning_item := cls.maybe_reasoning_message(item):
+                # Reconstruct thinking blocks from content (text) and encrypted_content (signature)
+                content_items = reasoning_item.get("content", [])
+                signature = reasoning_item.get("encrypted_content")
+
+                if content_items and preserve_thinking_blocks:
+                    # Reconstruct thinking blocks from content and signature
+                    pending_thinking_blocks = []
+                    for content_item in content_items:
+                        if (
+                            isinstance(content_item, dict)
+                            and content_item.get("type") == "reasoning_text"
+                        ):
+                            thinking_block = {
+                                "type": "thinking",
+                                "thinking": content_item.get("text", ""),
+                            }
+                            # Add signature if available
+                            if signature:
+                                thinking_block["signature"] = signature
+                            pending_thinking_blocks.append(thinking_block)
 
             # 8) If we haven't recognized it => fail or ignore
             else:
diff --git a/src/agents/models/chatcmpl_stream_handler.py b/src/agents/models/chatcmpl_stream_handler.py
index 359d47bb5..474bffe09 100644
--- a/src/agents/models/chatcmpl_stream_handler.py
+++ b/src/agents/models/chatcmpl_stream_handler.py
@@ -62,6 +62,9 @@ class StreamingState:
     # Fields for real-time function call streaming
     function_call_streaming: dict[int, bool] = field(default_factory=dict)
     function_call_output_idx: dict[int, int] = field(default_factory=dict)
+    # Store accumulated thinking text and signature for Anthropic compatibility
+    thinking_text: str = ""
+    thinking_signature: str | None = None
 
 
 class SequenceNumber:
@@ -101,6 +104,19 @@ async def handle_stream(
 
             delta = chunk.choices[0].delta
 
+            # Handle thinking blocks from Anthropic (for preserving signatures)
+            if hasattr(delta, "thinking_blocks") and delta.thinking_blocks:
+                for block in delta.thinking_blocks:
+                    if isinstance(block, dict):
+                        # Accumulate thinking text
+                        thinking_text = block.get("thinking", "")
+                        if thinking_text:
+                            state.thinking_text += thinking_text
+                        # Store signature if present
+                        signature = block.get("signature")
+                        if signature:
+                            state.thinking_signature = signature
+
             # Handle reasoning content for reasoning summaries
             if hasattr(delta, "reasoning_content"):
                 reasoning_content = delta.reasoning_content
@@ -527,7 +543,19 @@ async def handle_stream(
 
         # include Reasoning item if it exists
         if state.reasoning_content_index_and_output:
-            outputs.append(state.reasoning_content_index_and_output[1])
+            reasoning_item = state.reasoning_content_index_and_output[1]
+            # Store thinking text in content and signature in encrypted_content
+            if state.thinking_text:
+                # Add thinking text as a Content object
+                if not reasoning_item.content:
+                    reasoning_item.content = []
+                reasoning_item.content.append(
+                    Content(text=state.thinking_text, type="reasoning_text")
+                )
+            # Store signature in encrypted_content
+            if state.thinking_signature:
+                reasoning_item.encrypted_content = state.thinking_signature
+            outputs.append(reasoning_item)
 
         # include text or refusal content if they exist
         if state.text_content_index_and_output or state.refusal_content_index_and_output:
diff --git a/src/agents/realtime/__init__.py b/src/agents/realtime/__init__.py
index 7675c466f..3f0793fa1 100644
--- a/src/agents/realtime/__init__.py
+++ b/src/agents/realtime/__init__.py
@@ -3,6 +3,7 @@
     RealtimeAudioFormat,
     RealtimeClientMessage,
     RealtimeGuardrailsSettings,
+    RealtimeInputAudioNoiseReductionConfig,
     RealtimeInputAudioTranscriptionConfig,
     RealtimeModelName,
     RealtimeModelTracingConfig,
@@ -101,6 +102,7 @@
     "RealtimeAudioFormat",
     "RealtimeClientMessage",
     "RealtimeGuardrailsSettings",
+    "RealtimeInputAudioNoiseReductionConfig",
     "RealtimeInputAudioTranscriptionConfig",
     "RealtimeModelName",
     "RealtimeModelTracingConfig",
diff --git a/src/agents/realtime/config.py b/src/agents/realtime/config.py
index 8b70c872f..ddbf48bab 100644
--- a/src/agents/realtime/config.py
+++ b/src/agents/realtime/config.py
@@ -61,6 +61,13 @@ class RealtimeInputAudioTranscriptionConfig(TypedDict):
     """An optional prompt to guide transcription."""
 
 
+class RealtimeInputAudioNoiseReductionConfig(TypedDict):
+    """Noise reduction configuration for input audio."""
+
+    type: NotRequired[Literal["near_field", "far_field"]]
+    """Noise reduction mode to apply to input audio."""
+
+
 class RealtimeTurnDetectionConfig(TypedDict):
     """Turn detection config. Allows extra vendor keys if needed."""
 
@@ -119,6 +126,9 @@ class RealtimeSessionModelSettings(TypedDict):
     input_audio_transcription: NotRequired[RealtimeInputAudioTranscriptionConfig]
     """Configuration for transcribing input audio."""
 
+    input_audio_noise_reduction: NotRequired[RealtimeInputAudioNoiseReductionConfig | None]
+    """Noise reduction configuration for input audio."""
+
     turn_detection: NotRequired[RealtimeTurnDetectionConfig]
     """Configuration for detecting conversation turns."""
 
diff --git a/src/agents/realtime/openai_realtime.py b/src/agents/realtime/openai_realtime.py
index 4d6cf398c..50aaf3c4b 100644
--- a/src/agents/realtime/openai_realtime.py
+++ b/src/agents/realtime/openai_realtime.py
@@ -825,14 +825,24 @@ def _get_session_config(
             "output_audio_format",
             DEFAULT_MODEL_SETTINGS.get("output_audio_format"),
         )
+        input_audio_noise_reduction = model_settings.get(
+            "input_audio_noise_reduction",
+            DEFAULT_MODEL_SETTINGS.get("input_audio_noise_reduction"),
+        )
 
         input_audio_config = None
         if any(
             value is not None
-            for value in [input_audio_format, input_audio_transcription, turn_detection]
+            for value in [
+                input_audio_format,
+                input_audio_noise_reduction,
+                input_audio_transcription,
+                turn_detection,
+            ]
         ):
             input_audio_config = OpenAIRealtimeAudioInput(
                 format=to_realtime_audio_format(input_audio_format),
+                noise_reduction=cast(Any, input_audio_noise_reduction),
                 transcription=cast(Any, input_audio_transcription),
                 turn_detection=cast(Any, turn_detection),
             )
diff --git a/tests/realtime/test_openai_realtime.py b/tests/realtime/test_openai_realtime.py
index 34352df44..29b6fbd9a 100644
--- a/tests/realtime/test_openai_realtime.py
+++ b/tests/realtime/test_openai_realtime.py
@@ -1,3 +1,4 @@
+import json
 from typing import Any, cast
 from unittest.mock import AsyncMock, Mock, patch
 
@@ -96,6 +97,88 @@ def mock_create_task_func(coro):
         assert model._websocket_task is not None
         assert model.model == "gpt-4o-realtime-preview"
 
+    @pytest.mark.asyncio
+    async def test_session_update_includes_noise_reduction(self, model, mock_websocket):
+        """Session.update should pass through input_audio_noise_reduction config."""
+        config = {
+            "api_key": "test-api-key-123",
+            "initial_model_settings": {
+                "model_name": "gpt-4o-realtime-preview",
+                "input_audio_noise_reduction": {"type": "near_field"},
+            },
+        }
+
+        sent_messages: list[dict[str, Any]] = []
+
+        async def async_websocket(*args, **kwargs):
+            async def send(payload: str):
+                sent_messages.append(json.loads(payload))
+                return None
+
+            mock_websocket.send.side_effect = send
+            return mock_websocket
+
+        with patch("websockets.connect", side_effect=async_websocket):
+            with patch("asyncio.create_task") as mock_create_task:
+                mock_task = AsyncMock()
+
+                def mock_create_task_func(coro):
+                    coro.close()
+                    return mock_task
+
+                mock_create_task.side_effect = mock_create_task_func
+                await model.connect(config)
+
+        # Find the session.update events
+        session_updates = [m for m in sent_messages if m.get("type") == "session.update"]
+        assert len(session_updates) >= 1
+        # Verify the last session.update contains the noise_reduction field
+        session = session_updates[-1]["session"]
+        assert session.get("audio", {}).get("input", {}).get("noise_reduction") == {
+            "type": "near_field"
+        }
+
+    @pytest.mark.asyncio
+    async def test_session_update_omits_noise_reduction_when_not_provided(
+        self, model, mock_websocket
+    ):
+        """Session.update should omit input_audio_noise_reduction when not provided."""
+        config = {
+            "api_key": "test-api-key-123",
+            "initial_model_settings": {
+                "model_name": "gpt-4o-realtime-preview",
+            },
+        }
+
+        sent_messages: list[dict[str, Any]] = []
+
+        async def async_websocket(*args, **kwargs):
+            async def send(payload: str):
+                sent_messages.append(json.loads(payload))
+                return None
+
+            mock_websocket.send.side_effect = send
+            return mock_websocket
+
+        with patch("websockets.connect", side_effect=async_websocket):
+            with patch("asyncio.create_task") as mock_create_task:
+                mock_task = AsyncMock()
+
+                def mock_create_task_func(coro):
+                    coro.close()
+                    return mock_task
+
+                mock_create_task.side_effect = mock_create_task_func
+                await model.connect(config)
+
+        # Find the session.update events
+        session_updates = [m for m in sent_messages if m.get("type") == "session.update"]
+        assert len(session_updates) >= 1
+        # Verify the last session.update omits the noise_reduction field
+        session = session_updates[-1]["session"]
+        assert "audio" in session and "input" in session["audio"]
+        assert "noise_reduction" not in session["audio"]["input"]
+
     @pytest.mark.asyncio
     async def test_connect_with_custom_headers_overrides_defaults(self, model, mock_websocket):
         """If custom headers are provided, use them verbatim without adding defaults."""
diff --git a/tests/test_anthropic_thinking_blocks.py b/tests/test_anthropic_thinking_blocks.py
index 9513c7833..933be2c0e 100644
--- a/tests/test_anthropic_thinking_blocks.py
+++ b/tests/test_anthropic_thinking_blocks.py
@@ -10,7 +10,10 @@
 
 from __future__ import annotations
 
-from typing import Any
+from typing import Any, cast
+
+from openai.types.chat import ChatCompletionMessageToolCall
+from openai.types.chat.chat_completion_message_tool_call import Function
 
 from agents.extensions.models.litellm_model import InternalChatCompletionMessage
 from agents.models.chatcmpl_converter import Converter
@@ -99,3 +102,115 @@ def test_reasoning_items_preserved_in_message_conversion():
         thinking_block = reasoning_item.content[0]
         assert thinking_block.type == "reasoning_text"
         assert thinking_block.text == "I need to call the weather function for Paris"
+
+
+def test_anthropic_thinking_blocks_with_tool_calls():
+    """
+    Test for models with extended thinking and interleaved thinking with tool calls.
+
+    This test verifies the Anthropic's API's requirements for thinking blocks
+    to be the first content in assistant messages when reasoning is enabled and tool
+    calls are present.
+    """
+    # Create a message with reasoning, thinking blocks and tool calls
+    message = InternalChatCompletionMessage(
+        role="assistant",
+        content="I'll check the weather for you.",
+        reasoning_content="The user wants weather information, I need to call the weather function",
+        thinking_blocks=[
+            {
+                "type": "thinking",
+                "thinking": (
+                    "The user is asking about weather. "
+                    "Let me use the weather tool to get this information."
+                ),
+                "signature": "TestSignature123",
+            }
+        ],
+        tool_calls=[
+            ChatCompletionMessageToolCall(
+                id="call_123",
+                type="function",
+                function=Function(name="get_weather", arguments='{"city": "Tokyo"}'),
+            )
+        ],
+    )
+
+    # Step 1: Convert message to output items
+    output_items = Converter.message_to_output_items(message)
+
+    # Verify reasoning item exists and contains thinking blocks
+    reasoning_items = [
+        item for item in output_items if hasattr(item, "type") and item.type == "reasoning"
+    ]
+    assert len(reasoning_items) == 1, "Should have exactly one reasoning item"
+
+    reasoning_item = reasoning_items[0]
+
+    # Verify thinking text is stored in content
+    assert hasattr(reasoning_item, "content") and reasoning_item.content, (
+        "Reasoning item should have content"
+    )
+    assert reasoning_item.content[0].type == "reasoning_text", (
+        "Content should be reasoning_text type"
+    )
+
+    # Verify signature is stored in encrypted_content
+    assert hasattr(reasoning_item, "encrypted_content"), (
+        "Reasoning item should have encrypted_content"
+    )
+    assert reasoning_item.encrypted_content == "TestSignature123", "Signature should be preserved"
+
+    # Verify tool calls are present
+    tool_call_items = [
+        item for item in output_items if hasattr(item, "type") and item.type == "function_call"
+    ]
+    assert len(tool_call_items) == 1, "Should have exactly one tool call"
+
+    # Step 2: Convert output items back to messages
+    # Convert items to dicts for the converter (simulating serialization/deserialization)
+    items_as_dicts: list[dict[str, Any]] = []
+    for item in output_items:
+        if hasattr(item, "model_dump"):
+            items_as_dicts.append(item.model_dump())
+        else:
+            items_as_dicts.append(cast(dict[str, Any], item))
+
+    messages = Converter.items_to_messages(items_as_dicts, preserve_thinking_blocks=True)  # type: ignore[arg-type]
+
+    # Find the assistant message with tool calls
+    assistant_messages = [
+        msg for msg in messages if msg.get("role") == "assistant" and msg.get("tool_calls")
+    ]
+    assert len(assistant_messages) == 1, "Should have exactly one assistant message with tool calls"
+
+    assistant_msg = assistant_messages[0]
+
+    # Content must start with thinking blocks, not text
+    content = assistant_msg.get("content")
+    assert content is not None, "Assistant message should have content"
+
+    assert isinstance(content, list) and len(content) > 0, (
+        "Assistant message content should be a non-empty list"
+    )
+
+    first_content = content[0]
+    assert first_content.get("type") == "thinking", (
+        f"First content must be 'thinking' type for Anthropic compatibility, "
+        f"but got '{first_content.get('type')}'"
+    )
+    expected_thinking = (
+        "The user is asking about weather. Let me use the weather tool to get this information."
+    )
+    assert first_content.get("thinking") == expected_thinking, (
+        "Thinking content should be preserved"
+    )
+    # Signature should also be preserved
+    assert first_content.get("signature") == "TestSignature123", (
+        "Signature should be preserved in thinking block"
+    )
+
+    # Verify tool calls are preserved
+    tool_calls = assistant_msg.get("tool_calls", [])
+    assert len(cast(list[Any], tool_calls)) == 1, "Tool calls should be preserved"
+    assert cast(list[Any], tool_calls)[0]["function"]["name"] == "get_weather"