fix: forward service_tier param to Anthropic API (#23401)

* fix: forward service_tier param to Anthropic API

service_tier was silently dropped because it was missing from
AnthropicConfig.get_supported_openai_params(). Add it to the
supported params list and add a passthrough mapping in
map_openai_params() so it is forwarded as-is to the request body.

Fixes #23398

* fix: add service_tier to AnthropicMessagesRequestOptionalParams TypedDict

* fix: restrict service_tier to Anthropic-valid values only

* fix: honour drop_params flag for invalid service_tier values on Anthropic

When drop_params=False, passing an unrecognised service_tier (e.g. the
OpenAI-specific "default"/"flex"/"scale") now raises UnsupportedParamsError
instead of silently discarding the value. When drop_params=True the value
is still silently dropped. Also tightens the TypedDict field from Optional[str]
to Optional[Literal["auto", "standard_only"]] for static analysis.

Fixes https://github.com/BerriAI/litellm/issues/23398

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

* fix: silently drop unrecognised service_tier values for backward-compatibility

* fix: raise UnsupportedParamsError for invalid service_tier when drop_params=False

* values check for service tier

* verbose logging for supported values of service_tier

* support future additions to service_tier values

* fix: forward service_tier param to Anthropic API as pure passthrough

---------

Co-authored-by: Claude Sonnet 4.6 <noreply@anthropic.com>

litellm/llms/anthropic/chat/transformation.py

@@ -195,6 +195,7 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             "speed",
             "context_management",
             "cache_control",
+            "service_tier",
         ]
 
         if (
@@ -1068,6 +1069,11 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             elif param == "cache_control" and isinstance(value, dict):
                 # Pass through top-level cache_control for automatic prompt caching
                 optional_params["cache_control"] = value
+            elif param == "service_tier" and isinstance(value, str):
+                # Pass through service_tier to the Anthropic API.
+                # Anthropic validates the value and returns an error for
+                # unsupported tiers.
+                optional_params["service_tier"] = value
 
         ## handle thinking tokens
         self.update_optional_params_with_thinking_tokens(

litellm/types/llms/anthropic.py

@@ -364,6 +364,7 @@ class AnthropicMessagesRequestOptionalParams(TypedDict, total=False):
     speed: Optional[str]  # Fast mode support for Opus models
     output_config: Optional[AnthropicOutputConfig]  # Configuration for Claude's output behavior
     cache_control: Optional[Dict[str, Any]]  # Automatic prompt caching
+    service_tier: Optional[str]  # Service tier for priority capacity (e.g. "auto", "standard_only")
 
 
 class AnthropicMessagesRequest(AnthropicMessagesRequestOptionalParams, total=False):

tests/test_litellm/llms/anthropic/chat/test_anthropic_chat_transformation.py

@@ -3300,3 +3300,56 @@ def test_map_tool_helper_empty_parameters_get_default():
     assert result is not None
     assert result["input_schema"]["type"] == "object"
     assert result["input_schema"].get("properties") == {}
+
+
+@pytest.mark.parametrize("service_tier", ["auto", "standard_only"])
+def test_service_tier_forwarded_to_anthropic(service_tier: str):
+    """
+    service_tier must be forwarded as-is to the Anthropic API request body.
+
+    Fixes https://github.com/BerriAI/litellm/issues/23398
+    """
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"service_tier": service_tier},
+        optional_params={},
+        model="claude-sonnet-4-6",
+        drop_params=False,
+    )
+
+    assert result.get("service_tier") == service_tier
+
+
+def test_service_tier_in_supported_params():
+    """
+    service_tier must appear in get_supported_openai_params so it is not
+    silently dropped before map_openai_params is called.
+
+    Fixes https://github.com/BerriAI/litellm/issues/23398
+    """
+    config = AnthropicConfig()
+    assert "service_tier" in config.get_supported_openai_params(
+        model="claude-sonnet-4-6"
+    )
+
+
+@pytest.mark.parametrize("service_tier", ["default", "flex", "scale"])
+def test_service_tier_any_string_forwarded_to_anthropic(service_tier: str):
+    """
+    service_tier is forwarded as-is to the Anthropic API for all string
+    values. Anthropic validates the value and returns an error for
+    unsupported tiers.
+
+    Fixes https://github.com/BerriAI/litellm/issues/23398
+    """
+    config = AnthropicConfig()
+
+    result = config.map_openai_params(
+        non_default_params={"service_tier": service_tier},
+        optional_params={},
+        model="claude-sonnet-4-6",
+        drop_params=False,
+    )
+
+    assert result.get("service_tier") == service_tier