diff --git a/litellm/litellm_core_utils/prompt_templates/common_utils.py b/litellm/litellm_core_utils/prompt_templates/common_utils.py
index 387c072ffd7..a99a2677e8f 100644
--- a/litellm/litellm_core_utils/prompt_templates/common_utils.py
+++ b/litellm/litellm_core_utils/prompt_templates/common_utils.py
@@ -573,3 +573,12 @@ def get_tool_call_names(tools: List[ChatCompletionToolParam]) -> List[str]:
             if tool_call_name:
                 tool_call_names.append(tool_call_name)
     return tool_call_names
+
+
+def is_function_call(optional_params: dict) -> bool:
+    """
+    Checks if the optional params contain the function call
+    """
+    if "functions" in optional_params and optional_params.get("functions"):
+        return True
+    return False
diff --git a/litellm/litellm_core_utils/streaming_handler.py b/litellm/litellm_core_utils/streaming_handler.py
index dcc6ea36a30..5ae1dcf9889 100644
--- a/litellm/litellm_core_utils/streaming_handler.py
+++ b/litellm/litellm_core_utils/streaming_handler.py
@@ -149,14 +149,14 @@ class CustomStreamWrapper:
         )
 
     def check_is_function_call(self, logging_obj) -> bool:
+        from litellm.litellm_core_utils.prompt_templates.common_utils import (
+            is_function_call,
+        )
+
         if hasattr(logging_obj, "optional_params") and isinstance(
             logging_obj.optional_params, dict
         ):
-            if (
-                "litellm_param_is_function_call" in logging_obj.optional_params
-                and logging_obj.optional_params["litellm_param_is_function_call"]
-                is True
-            ):
+            if is_function_call(logging_obj.optional_params):
                 return True
 
         return False
diff --git a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
index cd67be3545a..902f8257248 100644
--- a/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
+++ b/litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py
@@ -455,9 +455,6 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 and value
             ):
                 optional_params["tools"] = self._map_function(value=value)
-                optional_params["litellm_param_is_function_call"] = (
-                    True if param == "functions" else False
-                )
             elif param == "tool_choice" and (
                 isinstance(value, str) or isinstance(value, dict)
             ):
@@ -880,8 +877,14 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
         else:
             return "stop"
 
-    def _process_candidates(self, _candidates, model_response, litellm_params):
+    def _process_candidates(
+        self, _candidates, model_response, standard_optional_params: dict
+    ):
         """Helper method to process candidates and extract metadata"""
+        from litellm.litellm_core_utils.prompt_templates.common_utils import (
+            is_function_call,
+        )
+
         grounding_metadata: List[dict] = []
         safety_ratings: List = []
         citation_metadata: List = []
@@ -918,9 +921,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 functions, tools = self._transform_parts(
                     parts=candidate["content"]["parts"],
                     index=candidate.get("index", idx),
-                    is_function_call=litellm_params.get(
-                        "litellm_param_is_function_call"
-                    ),
+                    is_function_call=is_function_call(standard_optional_params),
                 )
 
             if "logprobsResult" in candidate:
@@ -1019,7 +1020,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     safety_ratings,
                     citation_metadata,
                 ) = self._process_candidates(
-                    _candidates, model_response, litellm_params
+                    _candidates, model_response, logging_obj.optional_params
                 )
 
             usage = self._calculate_usage(completion_response=completion_response)