diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index a9fd0f4ea8a..bf0b2709365 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -8,9 +8,11 @@ from litellm._logging import verbose_logger
 from litellm.types.utils import (
     CacheCreationTokenDetails,
     CallTypes,
+    CompletionTokensDetailsWrapper,
     ImageResponse,
     ModelInfo,
     PassthroughCallTypes,
+    PromptTokensDetailsWrapper,
     ServiceTier,
     Usage,
 )
@@ -767,6 +769,64 @@ def generic_cost_per_token(  # noqa: PLR0915
     return prompt_cost, completion_cost
 
 
+def calculate_image_response_cost_from_usage(
+    model: str,
+    image_response: ImageResponse,
+    custom_llm_provider: str,
+) -> Optional[float]:
+    """
+    Calculate image generation cost from usage metadata when available.
+
+    Returns:
+        Optional[float]: total cost from token usage, or None when usage metadata
+        is missing/incomplete and caller should fall back to flat per-image pricing.
+    """
+    usage = image_response.usage
+    if usage is None:
+        return None
+
+    prompt_tokens = usage.input_tokens
+    completion_tokens = usage.output_tokens
+    total_tokens = usage.total_tokens
+
+    if prompt_tokens is None or completion_tokens is None or total_tokens is None:
+        return None
+
+    # ImageResponse may carry a default zeroed usage object even when provider
+    # usage metadata is absent. Treat this as missing usage and fall back.
+    if prompt_tokens == 0 and completion_tokens == 0 and total_tokens == 0:
+        return None
+
+    input_tokens_details = getattr(usage, "input_tokens_details", None)
+    prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
+    if input_tokens_details is not None:
+        prompt_tokens_details = PromptTokensDetailsWrapper(
+            text_tokens=getattr(input_tokens_details, "text_tokens", None),
+            image_tokens=getattr(input_tokens_details, "image_tokens", None),
+            cached_tokens=0,
+        )
+
+    normalized_usage = Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=completion_tokens,
+        total_tokens=total_tokens,
+        prompt_tokens_details=prompt_tokens_details,
+        completion_tokens_details=CompletionTokensDetailsWrapper(
+            text_tokens=0,
+            image_tokens=completion_tokens,
+            reasoning_tokens=0,
+            audio_tokens=0,
+        ),
+    )
+
+    prompt_cost, completion_cost = generic_cost_per_token(
+        model=model,
+        usage=normalized_usage,
+        custom_llm_provider=custom_llm_provider,
+    )
+    return prompt_cost + completion_cost
+
+
 class CostCalculatorUtils:
     @staticmethod
     def _call_type_has_image_response(call_type: str) -> bool:
diff --git a/litellm/llms/gemini/image_generation/cost_calculator.py b/litellm/llms/gemini/image_generation/cost_calculator.py
index 6d7572d5522..941ab0d50f7 100644
--- a/litellm/llms/gemini/image_generation/cost_calculator.py
+++ b/litellm/llms/gemini/image_generation/cost_calculator.py
@@ -2,71 +2,13 @@
 Google AI Image Generation Cost Calculator
 """
 
-from typing import Any, Optional
+from typing import Any
 
 import litellm
-from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
-from litellm.types.utils import (
-    CompletionTokensDetailsWrapper,
-    ImageResponse,
-    PromptTokensDetailsWrapper,
-    Usage,
+from litellm.litellm_core_utils.llm_cost_calc.utils import (
+    calculate_image_response_cost_from_usage,
 )
-
-
-def _calculate_token_based_cost(model: str, image_response: ImageResponse) -> Optional[float]:
-    """
-    Calculate token-based image generation cost when usage metadata is available.
-
-    Falls back to None when usage metadata is missing/incomplete.
-    """
-    usage = image_response.usage
-    if usage is None:
-        return None
-
-    prompt_tokens = usage.input_tokens
-    completion_tokens = usage.output_tokens
-    total_tokens = usage.total_tokens
-
-    if (
-        prompt_tokens is None
-        or completion_tokens is None
-        or total_tokens is None
-    ):
-        return None
-    # ImageResponse may carry a default zeroed usage object even when provider
-    # usage metadata is absent. Treat this as missing usage and fall back.
-    if prompt_tokens == 0 and completion_tokens == 0 and total_tokens == 0:
-        return None
-
-    input_tokens_details = getattr(usage, "input_tokens_details", None)
-    prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
-    if input_tokens_details is not None:
-        prompt_tokens_details = PromptTokensDetailsWrapper(
-            text_tokens=getattr(input_tokens_details, "text_tokens", None),
-            image_tokens=getattr(input_tokens_details, "image_tokens", None),
-            cached_tokens=0,
-        )
-
-    normalized_usage = Usage(
-        prompt_tokens=prompt_tokens,
-        completion_tokens=completion_tokens,
-        total_tokens=total_tokens,
-        prompt_tokens_details=prompt_tokens_details,
-        completion_tokens_details=CompletionTokensDetailsWrapper(
-            text_tokens=0,
-            image_tokens=completion_tokens,
-            reasoning_tokens=0,
-            audio_tokens=0,
-        ),
-    )
-
-    prompt_cost, completion_cost = generic_cost_per_token(
-        model=model,
-        usage=normalized_usage,
-        custom_llm_provider="gemini",
-    )
-    return prompt_cost + completion_cost
+from litellm.types.utils import ImageResponse
 
 
 def cost_calculator(
@@ -74,7 +16,7 @@ def cost_calculator(
     image_response: Any,
 ) -> float:
     """
-    Vertex AI Image Generation Cost Calculator
+    Google AI Image Generation Cost Calculator
     """
     _model_info = litellm.get_model_info(
         model=model,
@@ -82,8 +24,10 @@ def cost_calculator(
     )
 
     if isinstance(image_response, ImageResponse):
-        token_based_cost = _calculate_token_based_cost(
-            model=model, image_response=image_response
+        token_based_cost = calculate_image_response_cost_from_usage(
+            model=model,
+            image_response=image_response,
+            custom_llm_provider="gemini",
         )
         if token_based_cost is not None:
             return token_based_cost
diff --git a/litellm/llms/vertex_ai/image_generation/cost_calculator.py b/litellm/llms/vertex_ai/image_generation/cost_calculator.py
index ac587182f04..012de5498cb 100644
--- a/litellm/llms/vertex_ai/image_generation/cost_calculator.py
+++ b/litellm/llms/vertex_ai/image_generation/cost_calculator.py
@@ -2,71 +2,11 @@
 Vertex AI Image Generation Cost Calculator
 """
 
-from typing import Optional
-
 import litellm
-from litellm.litellm_core_utils.llm_cost_calc.utils import generic_cost_per_token
-from litellm.types.utils import (
-    CompletionTokensDetailsWrapper,
-    ImageResponse,
-    PromptTokensDetailsWrapper,
-    Usage,
+from litellm.litellm_core_utils.llm_cost_calc.utils import (
+    calculate_image_response_cost_from_usage,
 )
-
-
-def _calculate_token_based_cost(model: str, image_response: ImageResponse) -> Optional[float]:
-    """
-    Calculate token-based image generation cost when usage metadata is available.
-
-    Falls back to None when usage metadata is missing/incomplete.
-    """
-    usage = image_response.usage
-    if usage is None:
-        return None
-
-    prompt_tokens = usage.input_tokens
-    completion_tokens = usage.output_tokens
-    total_tokens = usage.total_tokens
-
-    if (
-        prompt_tokens is None
-        or completion_tokens is None
-        or total_tokens is None
-    ):
-        return None
-    # ImageResponse may carry a default zeroed usage object even when provider
-    # usage metadata is absent. Treat this as missing usage and fall back.
-    if prompt_tokens == 0 and completion_tokens == 0 and total_tokens == 0:
-        return None
-
-    input_tokens_details = getattr(usage, "input_tokens_details", None)
-    prompt_tokens_details: Optional[PromptTokensDetailsWrapper] = None
-    if input_tokens_details is not None:
-        prompt_tokens_details = PromptTokensDetailsWrapper(
-            text_tokens=getattr(input_tokens_details, "text_tokens", None),
-            image_tokens=getattr(input_tokens_details, "image_tokens", None),
-            cached_tokens=0,
-        )
-
-    normalized_usage = Usage(
-        prompt_tokens=prompt_tokens,
-        completion_tokens=completion_tokens,
-        total_tokens=total_tokens,
-        prompt_tokens_details=prompt_tokens_details,
-        completion_tokens_details=CompletionTokensDetailsWrapper(
-            text_tokens=0,
-            image_tokens=completion_tokens,
-            reasoning_tokens=0,
-            audio_tokens=0,
-        ),
-    )
-
-    prompt_cost, completion_cost = generic_cost_per_token(
-        model=model,
-        usage=normalized_usage,
-        custom_llm_provider="vertex_ai",
-    )
-    return prompt_cost + completion_cost
+from litellm.types.utils import ImageResponse
 
 
 def cost_calculator(
@@ -81,8 +21,10 @@ def cost_calculator(
         custom_llm_provider="vertex_ai",
     )
 
-    token_based_cost = _calculate_token_based_cost(
-        model=model, image_response=image_response
+    token_based_cost = calculate_image_response_cost_from_usage(
+        model=model,
+        image_response=image_response,
+        custom_llm_provider="vertex_ai",
     )
     if token_based_cost is not None:
         return token_based_cost