diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index 5542249d2c7..392c2be25fe 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -1277,9 +1277,14 @@ class CustomStreamWrapper:
             if _is_delta_empty:
                 model_response.choices[0].delta = Delta(content=self._close_open_think_block())
                 # get any function call arguments
-                model_response.choices[0].finish_reason = map_finish_reason(
+                _mapped_finish_reason: Final = map_finish_reason(
                     finish_reason=self.received_finish_reason
                 )  # ensure consistent output to openai
+                model_response.choices[0].finish_reason = (
+                    # the calls were streamed in an earlier chunk, so the terminal
+                    # reason says why the turn ended: tool_calls, not stop
+                    "tool_calls" if _mapped_finish_reason == "stop" and self.tool_call else _mapped_finish_reason
+                )
 
                 self.sent_last_chunk = True
 
@@ -1724,8 +1729,17 @@ class CustomStreamWrapper:
                     return
             ## CHECK FOR TOOL USE
 
-            if "tool_calls" in completion_obj and len(completion_obj["tool_calls"]) > 0:
-                if self.is_function_call is True:  # user passed in 'functions' param
+            # OpenAI-compatible providers deliver the calls on the delta, which is
+            # copied into model_response below the completion_obj in that branch, so
+            # checking completion_obj alone missed them and the stop -> tool_calls
+            # rewrite in finish_reason_handler() never fired for streamed calls.
+            tool_calls_in_completion_obj: Final = bool(
+                "tool_calls" in completion_obj and len(completion_obj["tool_calls"]) > 0
+            )
+            tool_calls_in_delta: Final = bool(model_response.choices and model_response.choices[0].delta.tool_calls)
+
+            if tool_calls_in_completion_obj or tool_calls_in_delta:
+                if self.is_function_call is True and tool_calls_in_completion_obj:  # user passed in 'functions' param
                     completion_obj["function_call"] = completion_obj["tool_calls"][0]["function"]
                     completion_obj["tool_calls"] = None
 
diff --git a/tests/unit/litellm_core_utils/test_streaming_handler.py b/tests/unit/litellm_core_utils/test_streaming_handler.py
index 41e8c50fa85..eb58452d289 100644
--- a/tests/unit/litellm_core_utils/test_streaming_handler.py
+++ b/tests/unit/litellm_core_utils/test_streaming_handler.py
@@ -8248,3 +8248,81 @@ def test_chunk_creator_usage_only_chunk_with_dict_usage_keeps_details(
     assert usage.prompt_tokens_details.cached_tokens == 80
     assert usage.completion_tokens_details is not None
     assert usage.completion_tokens_details.reasoning_tokens == 5
+
+
+def _tool_call_chunk(*, finish_reason: str | None = None) -> ModelResponseStream:
+    from litellm.types.utils import ChatCompletionDeltaToolCall, Function
+
+    return ModelResponseStream(
+        id="chatcmpl-tool-call",
+        created=1742056047,
+        model="gpt-4o-mini",
+        object="chat.completion.chunk",
+        choices=[
+            StreamingChoices(
+                index=0,
+                delta=Delta(
+                    role="assistant",
+                    content=None,
+                    tool_calls=[
+                        ChatCompletionDeltaToolCall(
+                            id="call_1",
+                            type="function",
+                            function=Function(name="get_weather", arguments="{}"),
+                            index=0,
+                        )
+                    ],
+                ),
+                finish_reason=finish_reason,
+            )
+        ],
+    )
+
+
+def _empty_stop_chunk() -> ModelResponseStream:
+    return ModelResponseStream(
+        id="chatcmpl-tool-call",
+        created=1742056047,
+        model="gpt-4o-mini",
+        object="chat.completion.chunk",
+        choices=[StreamingChoices(index=0, delta=Delta(content=""), finish_reason="stop")],
+    )
+
+
+@pytest.mark.parametrize(
+    "upstream_chunks",
+    [
+        [_tool_call_chunk(), _empty_stop_chunk()],
+        [_tool_call_chunk(finish_reason="stop")],
+    ],
+    ids=["separate-chunks", "same-chunk"],
+)
+def test_streamed_tool_call_keeps_tool_calls_finish_reason(
+    logging_obj: Logging, upstream_chunks: list[ModelResponseStream]
+):
+    """A streamed call ends with ``tool_calls``, like the non-streaming path.
+
+    Providers that are OpenAI-compatible answer a call with
+    ``finish_reason: "stop"``.  The non-streaming path rewrites that to
+    ``tool_calls``, and so does ``finish_reason_handler()`` for the synthetic
+    terminal chunk -- but an OpenAI-compatible stream never reached either: the
+    rewrite depended on ``self.tool_call``, which was only set from
+    ``completion_obj`` while those chunks carry the calls on the delta.
+    """
+    wrapper: Final = CustomStreamWrapper(
+        completion_stream=ModelResponseListIterator(model_responses=upstream_chunks),
+        model="gpt-4o-mini",
+        logging_obj=logging_obj,
+        custom_llm_provider="openai",
+    )
+
+    emitted: Final = list(wrapper)
+    terminal: Final = [
+        chunk.choices[0].finish_reason for chunk in emitted if chunk.choices and chunk.choices[0].finish_reason
+    ]
+
+    assert terminal == ["tool_calls"]
+
+    built: Final = litellm.stream_chunk_builder(chunks=wrapper.chunks)
+    assert built is not None
+    assert built.choices[0].finish_reason == "tool_calls"
\ No newline at end of file