diff --git a/libs/partners/openai/langchain_openai/chat_models/base.py b/libs/partners/openai/langchain_openai/chat_models/base.py
index 84459cc4d29..7133956bb40 100644
--- a/libs/partners/openai/langchain_openai/chat_models/base.py
+++ b/libs/partners/openai/langchain_openai/chat_models/base.py
@@ -1162,29 +1162,6 @@ class ChatOpenAI(BaseChatOpenAI):
         """Return whether this model can be serialized by Langchain."""
         return True
 
-    def _stream(self, *args: Any, **kwargs: Any) -> Iterator[ChatGenerationChunk]:
-        """Set default stream_options."""
-        default_stream_options = {"include_usage": True}
-        stream_options = kwargs.get("stream_options", {})
-        merged_stream_options = {**default_stream_options, **stream_options}
-        kwargs["stream_options"] = merged_stream_options
-
-        return super()._stream(*args, **kwargs)
-
-    async def _astream(
-        self,
-        *args: Any,
-        **kwargs: Any,
-    ) -> AsyncIterator[ChatGenerationChunk]:
-        """Set default stream_options."""
-        default_stream_options = {"include_usage": True}
-        stream_options = kwargs.get("stream_options", {})
-        merged_stream_options = {**default_stream_options, **stream_options}
-        kwargs["stream_options"] = merged_stream_options
-
-        async for chunk in super()._astream(*args, **kwargs):
-            yield chunk
-
 
 def _is_pydantic_class(obj: Any) -> bool:
     return isinstance(obj, type) and issubclass(obj, BaseModel)
diff --git a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
index db87949eb64..5a31bc76f9d 100644
--- a/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
+++ b/libs/partners/openai/tests/integration_tests/chat_models/test_base.py
@@ -346,10 +346,18 @@ def test_stream() -> None:
     llm = ChatOpenAI()
 
     full: Optional[BaseMessageChunk] = None
-    chunks_with_token_counts = 0
     for chunk in llm.stream("I'm Pickle Rick"):
         assert isinstance(chunk.content, str)
         full = chunk if full is None else full + chunk
+    assert isinstance(full, AIMessageChunk)
+    assert full.response_metadata.get("finish_reason") is not None
+
+    # check token usage
+    aggregate: Optional[BaseMessageChunk] = None
+    chunks_with_token_counts = 0
+    for chunk in llm.stream("Hello", stream_options={"include_usage": True}):
+        assert isinstance(chunk.content, str)
+        aggregate = chunk if aggregate is None else aggregate + chunk
         assert isinstance(chunk, AIMessageChunk)
         if chunk.usage_metadata is not None:
             chunks_with_token_counts += 1
@@ -359,21 +367,11 @@ def test_stream() -> None:
             "AIMessageChunk aggregation adds counts. Check that "
             "this is behaving properly."
         )
-
-    # check token usage is populated
-    assert isinstance(full, AIMessageChunk)
-    assert full.usage_metadata is not None
-    assert full.usage_metadata["input_tokens"] > 0
-    assert full.usage_metadata["output_tokens"] > 0
-    assert full.usage_metadata["total_tokens"] > 0
-
-    # check not populated
-    aggregate: Optional[BaseMessageChunk] = None
-    for chunk in llm.stream("Hello", stream_options={"include_usage": False}):
-        assert isinstance(chunk.content, str)
-        aggregate = chunk if aggregate is None else aggregate + chunk
     assert isinstance(aggregate, AIMessageChunk)
-    assert aggregate.usage_metadata is None
+    assert aggregate.usage_metadata is not None
+    assert aggregate.usage_metadata["input_tokens"] > 0
+    assert aggregate.usage_metadata["output_tokens"] > 0
+    assert aggregate.usage_metadata["total_tokens"] > 0
 
 
 async def test_astream() -> None:
@@ -381,10 +379,18 @@ async def test_astream() -> None:
     llm = ChatOpenAI()
 
     full: Optional[BaseMessageChunk] = None
-    chunks_with_token_counts = 0
     async for chunk in llm.astream("I'm Pickle Rick"):
         assert isinstance(chunk.content, str)
         full = chunk if full is None else full + chunk
+    assert isinstance(full, AIMessageChunk)
+    assert full.response_metadata.get("finish_reason") is not None
+
+    # check token usage
+    aggregate: Optional[BaseMessageChunk] = None
+    chunks_with_token_counts = 0
+    async for chunk in llm.astream("Hello", stream_options={"include_usage": True}):
+        assert isinstance(chunk.content, str)
+        aggregate = chunk if aggregate is None else aggregate + chunk
         assert isinstance(chunk, AIMessageChunk)
         if chunk.usage_metadata is not None:
             chunks_with_token_counts += 1
@@ -394,21 +400,11 @@ async def test_astream() -> None:
             "AIMessageChunk aggregation adds counts. Check that "
             "this is behaving properly."
         )
-
-    # check token usage is populated
-    assert isinstance(full, AIMessageChunk)
-    assert full.usage_metadata is not None
-    assert full.usage_metadata["input_tokens"] > 0
-    assert full.usage_metadata["output_tokens"] > 0
-    assert full.usage_metadata["total_tokens"] > 0
-
-    # check not populated
-    aggregate: Optional[BaseMessageChunk] = None
-    async for chunk in llm.astream("Hello", stream_options={"include_usage": False}):
-        assert isinstance(chunk.content, str)
-        aggregate = chunk if aggregate is None else aggregate + chunk
     assert isinstance(aggregate, AIMessageChunk)
-    assert aggregate.usage_metadata is None
+    assert aggregate.usage_metadata is not None
+    assert aggregate.usage_metadata["input_tokens"] > 0
+    assert aggregate.usage_metadata["output_tokens"] > 0
+    assert aggregate.usage_metadata["total_tokens"] > 0
 
 
 async def test_abatch() -> None: