diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index cb1b68903b1..5ef15b8a608 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -251,8 +251,6 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
         message_dict["role"] = "user"
     elif isinstance(message, AIMessage):
         message_dict["role"] = "assistant"
-        if "function_call" in message.additional_kwargs:
-            message_dict["function_call"] = message.additional_kwargs["function_call"]
         if message.tool_calls or message.invalid_tool_calls:
             message_dict["tool_calls"] = [
                 _lc_tool_call_to_openai_tool_call(tc) for tc in message.tool_calls
@@ -267,6 +265,10 @@ def _convert_message_to_dict(message: BaseMessage) -> dict:
                 {k: v for k, v in tool_call.items() if k in tool_call_supported_props}
                 for tool_call in message_dict["tool_calls"]
             ]
+        elif "function_call" in message.additional_kwargs:
+            # OpenAI raises 400 if both function_call and tool_calls are present in the
+            # same message.
+            message_dict["function_call"] = message.additional_kwargs["function_call"]
         else:
             pass
         # If tool calls present, content null value should be None not empty string.
diff --git a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
index c762b3dab5d..2331d465ef8 100644
--- a/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/unit_tests/chat_models/test_base.py
@@ -614,6 +614,50 @@ def test_openai_invoke_name(mock_client: MagicMock) -> None:
         assert res.name == "Erick"
 
 
+def test_function_calls_with_tool_calls(mock_client: MagicMock) -> None:
+    # Test that we ignore function calls if tool_calls are present
+    llm = ChatOpenAI(model="gpt-4.1-mini")
+    tool_call_message = AIMessage(
+        content="",
+        additional_kwargs={
+            "function_call": {
+                "name": "get_weather",
+                "arguments": '{"location": "Boston"}',
+            }
+        },
+        tool_calls=[
+            {
+                "name": "get_weather",
+                "args": {"location": "Boston"},
+                "id": "abc123",
+                "type": "tool_call",
+            }
+        ],
+    )
+    messages = [
+        HumanMessage("What's the weather in Boston?"),
+        tool_call_message,
+        ToolMessage(content="It's sunny.", name="get_weather", tool_call_id="abc123"),
+    ]
+    with patch.object(llm, "client", mock_client):
+        _ = llm.invoke(messages)
+        _, call_kwargs = mock_client.create.call_args
+        call_messages = call_kwargs["messages"]
+        tool_call_message_payload = call_messages[1]
+        assert "tool_calls" in tool_call_message_payload
+        assert "function_call" not in tool_call_message_payload
+
+    # Test we don't ignore function calls if tool_calls are not present
+    cast(AIMessage, messages[1]).tool_calls = []
+    with patch.object(llm, "client", mock_client):
+        _ = llm.invoke(messages)
+        _, call_kwargs = mock_client.create.call_args
+        call_messages = call_kwargs["messages"]
+        tool_call_message_payload = call_messages[1]
+        assert "function_call" in tool_call_message_payload
+        assert "tool_calls" not in tool_call_message_payload
+
+
 def test_custom_token_counting() -> None:
     def token_encoder(text: str) -> list[int]:
         return [1, 2, 3]