diff --git a/litellm/llms/anthropic/chat/transformation.py b/litellm/llms/anthropic/chat/transformation.py
index b442d27c91e..c1b2cb2985e 100644
--- a/litellm/llms/anthropic/chat/transformation.py
+++ b/litellm/llms/anthropic/chat/transformation.py
@@ -75,12 +75,12 @@ from litellm.types.utils import Message as LitellmMessage
 from litellm.utils import (
     ModelResponse,
     Usage,
-    _supports_factory,
     add_dummy_tool,
     any_assistant_message_has_thinking_blocks,
     get_max_tokens,
     has_tool_call_blocks,
     last_assistant_with_tool_calls_has_no_thinking_blocks,
+    supports_mid_conversation_system,
     supports_reasoning,
     token_counter,
 )
@@ -1911,10 +1911,8 @@ class AnthropicConfig(AnthropicModelInfo, BaseConfig):
             optional_params["system"] = anthropic_system_message_list
         conversation: Final = place_mid_conversation_system(
             later_messages,
-            supports_mid_conversation_system=_supports_factory(
-                model=model,
-                custom_llm_provider=self.custom_llm_provider,
-                key="supports_mid_conversation_system",
+            supports_mid_conversation_system=supports_mid_conversation_system(
+                model=model, custom_llm_provider=self.custom_llm_provider
             ),
         )
         # Format rest of message according to anthropic guidelines
diff --git a/litellm/utils.py b/litellm/utils.py
index c2770a1a26d..727ea140ee4 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -2722,6 +2722,15 @@ def supports_reasoning(model: str, custom_llm_provider: str | None = None) -> bo
     return _supports_factory(model=model, custom_llm_provider=custom_llm_provider, key="supports_reasoning")
 
 
+def supports_mid_conversation_system(model: str, custom_llm_provider: str | None = None) -> bool:
+    """
+    Check if the given model accepts role=system messages after the first turn and return a boolean value.
+    """
+    return _supports_factory(
+        model=model, custom_llm_provider=custom_llm_provider, key="supports_mid_conversation_system"
+    )
+
+
 def supports_native_structured_output(model: str, custom_llm_provider: str | None = None) -> bool:
     """
     Check if the given model supports native structured outputs and return a boolean value.