chore: add defaut/standard mapping, and some tests

docs/my-website/docs/providers/gemini.md

@@ -306,7 +306,10 @@ LiteLLM propagates OpenAI's `service_tier` parameter to Gemini, and also extract
 | `"auto"`              | `"priority"`          | LiteLLM maps OpenAI's `"auto"` to Gemini's `"priority"` tier, as `priority` will fall back on Gemini. |
 | `"flex"`              | `"flex"`              | Direct mapping. |
 | `"priority"`          | `"priority"`          | Direct mapping. |
-| Any other value       | Passed as-is          | Unrecognized values are sent blindly so that the provider can reject the request directly if invalid. |
+| `"default"`           | `"standard"`          | LiteLLM maps `"default"` to `"standard"`. |
+| Any other value       | Passed as-is (lowercased) | Values are case-insensitive and normalized to lowercase. |
+
+On the response, LiteLLM maps `"standard"` back to `"default"` for the Gemini API.
 
 
 ## Text-to-Speech (TTS) Audio Output

litellm/llms/vertex_ai/gemini/transformation.py

@@ -765,7 +765,13 @@ def _transform_request_body(  # noqa: PLR0915
             data["cachedContent"] = cached_content
         
         if service_tier := optional_params.pop("service_tier", None):
-            data["serviceTier"] = service_tier
+            if isinstance(service_tier, str):
+                if service_tier.lower() == "default":
+                    data["serviceTier"] = "standard"
+                else:
+                    data["serviceTier"] = service_tier.lower()
+            else:
+                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:

litellm/llms/vertex_ai/gemini/vertex_and_google_ai_studio_gemini.py

@@ -2432,7 +2432,13 @@ class VertexGeminiConfig(VertexAIBaseConfig, BaseConfig):
             ## ADD SERVICE TIER ##
             if getattr(raw_response, "headers", None):
                 if service_tier := raw_response.headers.get("x-gemini-service-tier"):
-                    setattr(model_response, "service_tier", service_tier)
+                    if isinstance(service_tier, str):
+                        if service_tier.lower() == "standard":
+                            setattr(model_response, "service_tier", "default")
+                        else:
+                            setattr(model_response, "service_tier", service_tier.lower())
+                    else:
+                        setattr(model_response, "service_tier", service_tier)
 
         except Exception as e:
             raise VertexAIError(
@@ -3167,7 +3173,13 @@ class ModelResponseIterator:
 
             service_tier = self.response_headers.get("x-gemini-service-tier")
             if service_tier:
-                setattr(model_response, "service_tier", service_tier)
+                if isinstance(service_tier, str):
+                    if service_tier.lower() == "standard":
+                        setattr(model_response, "service_tier", "default")
+                    else:
+                        setattr(model_response, "service_tier", service_tier.lower())
+                else:
+                    setattr(model_response, "service_tier", service_tier)
 
             setattr(model_response, "usage", usage)  # type: ignore
 

tests/test_gemini_service_tier.py

@@ -0,0 +1,112 @@
+import pytest
+from unittest.mock import MagicMock
+from litellm.llms.vertex_ai.gemini.transformation import _transform_request_body
+from litellm.llms.vertex_ai.gemini.vertex_and_google_ai_studio_gemini import VertexGeminiConfig, ModelResponseIterator
+from litellm.types.utils import ModelResponse
+
+def test_gemini_service_tier_request_mapping():
+    """Test that default service_tier is mapped to standard for Gemini API, case-insensitively."""
+    messages = [{"role": "user", "content": "test"}]
+    litellm_params = {}
+
+    # Test default -> standard
+    optional_params = {"service_tier": "default"}
+    result = _transform_request_body(
+        messages=messages,
+        model="gemini-2.5-pro",
+        optional_params=optional_params,
+        custom_llm_provider="gemini",
+        litellm_params=litellm_params,
+        cached_content=None,
+    )
+    assert result["serviceTier"] == "standard"
+
+    # Test DEFAULT -> standard
+    optional_params = {"service_tier": "DEFAULT"}
+    result = _transform_request_body(
+        messages=messages,
+        model="gemini-2.5-pro",
+        optional_params=optional_params,
+        custom_llm_provider="gemini",
+        litellm_params=litellm_params,
+        cached_content=None,
+    )
+    assert result["serviceTier"] == "standard"
+
+    # Test flex -> flex
+    optional_params = {"service_tier": "FLEX"}
+    result = _transform_request_body(
+        messages=messages,
+        model="gemini-2.5-pro",
+        optional_params=optional_params,
+        custom_llm_provider="gemini",
+        litellm_params=litellm_params,
+        cached_content=None,
+    )
+    assert result["serviceTier"] == "flex"
+
+def test_gemini_service_tier_response_mapping():
+    """Test that standard service_tier is mapped back to default for Gemini API, case-insensitively."""
+    config = VertexGeminiConfig()
+    raw_response = MagicMock()
+    raw_response.headers = {"x-gemini-service-tier": "STANDARD"}
+    
+    logging_obj = MagicMock()
+    logging_obj.custom_llm_provider = "gemini"
+
+    completion_response = {
+        "candidates": [{"content": {"parts": [{"text": "hi"}], "role": "model"}, "finishReason": "STOP"}],
+        "usageMetadata": {"promptTokenCount": 1, "candidatesTokenCount": 1, "totalTokenCount": 2}
+    }
+
+    result = config._transform_google_generate_content_to_openai_model_response(
+        completion_response=completion_response,
+        model_response=ModelResponse(),
+        model="gemini-pro",
+        logging_obj=logging_obj,
+        raw_response=raw_response
+    )
+
+    assert result.service_tier == "default"
+
+    # Test with lowercase standard
+    raw_response.headers = {"x-gemini-service-tier": "standard"}
+    result = config._transform_google_generate_content_to_openai_model_response(
+        completion_response=completion_response,
+        model_response=ModelResponse(),
+        model="gemini-pro",
+        logging_obj=logging_obj,
+        raw_response=raw_response
+    )
+    assert result.service_tier == "default"
+
+    # Test with flex -> flex
+    raw_response.headers = {"x-gemini-service-tier": "FLEX"}
+    result = config._transform_google_generate_content_to_openai_model_response(
+        completion_response=completion_response,
+        model_response=ModelResponse(),
+        model="gemini-pro",
+        logging_obj=logging_obj,
+        raw_response=raw_response
+    )
+    assert result.service_tier == "flex"
+
+def test_gemini_service_tier_streaming_response_mapping():
+    """Test streaming response mapping."""
+    logging_obj = MagicMock()
+    logging_obj.custom_llm_provider = "gemini"
+
+    iterator = ModelResponseIterator(
+        streaming_response=[],
+        sync_stream=True,
+        logging_obj=logging_obj,
+        response_headers={"x-gemini-service-tier": "STANDARD"}
+    )
+
+    chunk = {
+        "candidates": [{"content": {"parts": [{"text": "hi"}]}}],
+        "usageMetadata": {"promptTokenCount": 1, "candidatesTokenCount": 1, "totalTokenCount": 2}
+    }
+
+    result = iterator.chunk_parser(chunk)
+    assert result.service_tier == "default"

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

@@ -3522,7 +3522,7 @@ def test_vertex_ai_service_tier_streaming():
     )
     result = iterator.chunk_parser(chunk)
 
-    assert result.service_tier == "FLEX"
+    assert result.service_tier == "flex"
 
 
 def test_vertex_ai_service_tier_non_streaming():
@@ -3561,7 +3561,7 @@ def test_vertex_ai_service_tier_non_streaming():
         encoding=None,
     )
 
-    assert result.service_tier == "FLEX"
+    assert result.service_tier == "flex"
 
 
 def test_vertex_ai_traffic_type_surfaced_in_responses_api():