diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index bdbaee00c19..1fbc82c95a2 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -72,6 +72,11 @@ def _get_token_detail_value(details: object, key: str) -> int | None:
     return value if isinstance(value, int) else None
 
 
+def _requested_image_param(optional_params: Mapping[str, object] | None, key: str) -> str | None:
+    value: Final = None if optional_params is None else optional_params.get(key)
+    return value if isinstance(value, str) else None
+
+
 def get_web_search_requests(server_tool_use: Any) -> int | None:
     """
     Tolerantly read ``web_search_requests`` from a ``server_tool_use`` value
@@ -1311,12 +1316,13 @@ class CostCalculatorUtils:
             cost_calculator as vertex_ai_image_cost_calculator,
         )
 
-        if size is None:
-            size = completion_response.size or "1024-x-1024"
-        if quality is None:
-            quality = completion_response.quality or "standard"
-        if n is None:
-            n = len(completion_response.data) if completion_response.data else 0
+        resolved_size: Final = (
+            size or completion_response.size or _requested_image_param(optional_params, "size") or "1024-x-1024"
+        )
+        resolved_quality: Final = (
+            quality or completion_response.quality or _requested_image_param(optional_params, "quality") or "standard"
+        )
+        resolved_n: Final = n if n is not None else (len(completion_response.data) if completion_response.data else 0)
 
         if custom_llm_provider == litellm.LlmProviders.VERTEX_AI.value:
             if isinstance(completion_response, ImageResponse):
@@ -1328,7 +1334,7 @@ class CostCalculatorUtils:
             if isinstance(completion_response, ImageResponse):
                 return bedrock_image_cost_calculator(
                     model=model,
-                    size=size,
+                    size=resolved_size,
                     image_response=completion_response,
                     optional_params=optional_params,
                 )
@@ -1424,19 +1430,19 @@ class CostCalculatorUtils:
             # Fall through to default for DALL-E models
             return default_image_cost_calculator(
                 model=model,
-                quality=quality,
+                quality=resolved_quality,
                 custom_llm_provider=custom_llm_provider,
-                n=n,
-                size=size,
+                n=resolved_n,
+                size=resolved_size,
                 optional_params=optional_params,
             )
         else:
             return default_image_cost_calculator(
                 model=model,
-                quality=quality,
+                quality=resolved_quality,
                 custom_llm_provider=custom_llm_provider,
-                n=n,
-                size=size,
+                n=resolved_n,
+                size=resolved_size,
                 optional_params=optional_params,
             )
         return 0.0
diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
index ce90719789a..722c93cdb11 100644
--- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
+++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
@@ -27,6 +27,7 @@ from litellm.types.utils import (
 )
 
 from litellm.litellm_core_utils.llm_cost_calc.utils import (
+    CostCalculatorUtils,
     PromptTokensDetailsResult,
     TokenTypeCostBreakdown,
     _calculate_input_cost,
@@ -3841,3 +3842,40 @@ def test_generic_cost_per_token_grok_46_long_context(_local_model_cost_map):
     )
     assert prompt_cost == pytest.approx(200_000 * 4e-06 + 50_000 * 1e-06)
     assert completion_cost == pytest.approx(1_000 * 1.2e-05)
+
+
+@pytest.mark.parametrize(
+    ("response_quality", "requested_quality", "expected_cost"),
+    [
+        (None, "low", 0.04),
+        (None, None, 0.06),
+        ("high", "low", 0.08),
+    ],
+)
+def test_route_image_generation_cost_falls_back_to_requested_quality(
+    monkeypatch, response_quality, requested_quality, expected_cost
+):
+    def tier(cost):
+        return {"litellm_provider": "xai", "mode": "image_generation", "input_cost_per_image": cost}
+
+    monkeypatch.setattr(
+        litellm,
+        "model_cost",
+        {
+            "xai/grok-imagine-image-2.0": tier(0.06),
+            "low/1024-x-1024/grok-imagine-image-2.0": tier(0.04),
+            "high/1024-x-1024/grok-imagine-image-2.0": tier(0.08),
+        },
+    )
+    response = ImageResponse(data=[ImageObject(url="https://example.com/image.png")], quality=response_quality)
+    optional_params = {} if requested_quality is None else {"quality": requested_quality}
+
+    cost = CostCalculatorUtils.route_image_generation_cost_calculator(
+        model="xai/grok-imagine-image-2.0",
+        completion_response=response,
+        custom_llm_provider="xai",
+        optional_params=optional_params,
+        call_type="image_generation",
+    )
+
+    assert cost == expected_cost