feat: support service_tier in gemini

litellm/llms/gemini/chat/transformation.py

@@ -91,6 +91,7 @@ class GoogleAIStudioGeminiConfig(VertexGeminiConfig):
             "modalities",
             "parallel_tool_calls",
             "web_search_options",
+            "service_tier",
         ]
         if supports_reasoning(model, custom_llm_provider="gemini"):
             supported_params.append("reasoning_effort")

litellm/llms/vertex_ai/gemini/transformation.py

@@ -763,6 +763,10 @@ def _transform_request_body(  # noqa: PLR0915
             data["generationConfig"] = generation_config
         if cached_content is not None:
             data["cachedContent"] = cached_content
+        
+        if service_tier := optional_params.pop("service_tier", None):
+            data["serviceTier"] = service_tier
+
         # Only add labels for Vertex AI endpoints (not Google GenAI/AI Studio) and only if non-empty
         if labels and custom_llm_provider != LlmProviders.GEMINI:
             data["labels"] = labels

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -318,6 +318,7 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             "parallel_tool_calls",
             "web_search_options",
             "include_server_side_tool_invocations",
+            "service_tier",
         ]
 
         # Add penalty parameters only for non-preview models
@@ -1121,6 +1122,8 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                 optional_params = self._add_tools_to_optional_params(
                     optional_params, [_tools]
                 )
+            elif param == "service_tier" and isinstance(value, str):
+                optional_params["service_tier"] = value
             elif param == "include_server_side_tool_invocations" and value is True:
                 optional_params["include_server_side_tool_invocations"] = True
         if litellm.vertex_ai_safety_settings is not None:
@@ -2415,6 +2418,10 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
                     "provider_specific_fields", {}
                 )["traffic_type"] = traffic_type
 
+            ## ADD SERVICE TIER ##
+            if "serviceTier" in completion_response:
+                setattr(model_response, "service_tier", completion_response["serviceTier"])
+
         except Exception as e:
             raise VertexAIError(
                 message="Received={}, Error converting to valid response block={}. File an issue if litellm error - https://github.com/BerriAI/litellm/issues".format(
@@ -3139,6 +3146,9 @@ class ModelResponseIterator:
                         "provider_specific_fields", {}
                     )["traffic_type"] = traffic_type
 
+            if "serviceTier" in processed_chunk:
+                setattr(model_response, "service_tier", processed_chunk["serviceTier"])
+
             setattr(model_response, "usage", usage)  # type: ignore
 
             model_response._hidden_params["is_finished"] = False

litellm/types/llms/vertex_ai.py

@@ -325,6 +325,7 @@ class RequestBody(TypedDict, total=False):
     generationConfig: GenerationConfig
     cachedContent: str
     labels: Dict[str, str]
+    serviceTier: str
 
 
 class CachedContentRequestBody(TypedDict, total=False):

tests/test_litellm/llms/vertex_ai/gemini/test_vertex_ai_gemini_transformation.py

@@ -127,6 +127,24 @@ def test_vertex_ai_includes_labels():
     assert result["labels"] == {"project": "test", "team": "ai"}
 
 
+def test_service_tier_forwarded_to_vertex_ai():
+    """Test that service_tier in optional_params is mapped to serviceTier in request body."""
+    messages = [{"role": "user", "content": "test"}]
+    optional_params = {"service_tier": "flex"}
+    litellm_params = {}
+
+    result = _transform_request_body(
+        messages=messages,
+        model="gemini-2.5-pro",
+        optional_params=optional_params,
+        custom_llm_provider="vertex_ai",
+        litellm_params=litellm_params,
+        cached_content=None,
+    )
+
+    assert "serviceTier" in result
+    assert result["serviceTier"] == "flex"
+
 
 def test_extra_body_cache_not_forwarded_to_vertex_ai():
     """

tests/test_litellm/llms/vertex_ai/gemini/test_vertex_and_google_ai_studio_gemini.py

@@ -3504,6 +3504,64 @@ def test_vertex_ai_traffic_type_preserved_in_hidden_params_non_streaming():
     assert result._hidden_params["provider_specific_fields"]["traffic_type"] == "PROVISIONED_THROUGHPUT"
 
 
+def test_vertex_ai_service_tier_streaming():
+    """Test serviceTier is preserved in model_response for streaming."""
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        ModelResponseIterator,
+    )
+
+    chunk = {
+        "candidates": [{"content": {"parts": [{"text": "Hello"}]}}],
+        "serviceTier": "FLEX",
+    }
+
+    iterator = ModelResponseIterator(
+        streaming_response=[], sync_stream=True, logging_obj=MagicMock()
+    )
+    result = iterator.chunk_parser(chunk)
+
+    assert result.service_tier == "FLEX"
+
+
+def test_vertex_ai_service_tier_non_streaming():
+    """Test serviceTier is preserved in model_response for non-streaming."""
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+
+    completion_response = {
+        "candidates": [
+            {
+                "content": {"parts": [{"text": "Hello"}], "role": "model"},
+                "finishReason": "STOP",
+            }
+        ],
+        "usageMetadata": {
+            "promptTokenCount": 50,
+            "candidatesTokenCount": 100,
+            "totalTokenCount": 150,
+        },
+        "serviceTier": "FLEX",
+    }
+
+    raw_response = MagicMock()
+    raw_response.json.return_value = completion_response
+
+    result = VertexGeminiConfig().transform_response(
+        model="gemini-pro",
+        raw_response=raw_response,
+        model_response=ModelResponse(),
+        logging_obj=MagicMock(),
+        request_data={},
+        messages=[],
+        optional_params={},
+        litellm_params={},
+        encoding=None,
+    )
+
+    assert result.service_tier == "FLEX"
+
+
 def test_vertex_ai_traffic_type_surfaced_in_responses_api():
     """Test trafficType is surfaced as provider_specific_fields in ResponsesAPIResponse."""
     from litellm.responses.litellm_completion_transformation.transformation import (
@@ -3609,6 +3667,26 @@ def test_vertex_ai_web_search_options_in_map_openai_params():
     assert "web_search_options" not in optional_params, "web_search_options should be removed after transformation"
 
 
+def test_vertex_ai_service_tier_in_map_openai_params():
+    """Test that service_tier is correctly mapped to optional_params."""
+    from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import (
+        VertexGeminiConfig,
+    )
+
+    v = VertexGeminiConfig()
+    optional_params = {}
+    non_default_params = {"service_tier": "FLEX"}
+
+    result = v.map_openai_params(
+        non_default_params=non_default_params,
+        optional_params=optional_params,
+        model="gemini-pro",
+        drop_params=True,
+    )
+
+    assert result["service_tier"] == "FLEX"
+
+
 def test_vertex_ai_usage_metadata_with_video_tokens_in_prompt():
     """Test promptTokensDetails with VIDEO modality for video inputs.