fix(cost): apply tiered input rate to image tokens above threshold

gemini-2.5-pro and other models charge 2x input_cost_per_token above 200k
input tokens. Text tokens correctly pick up the tiered rate via
prompt_base_cost (resolved in _get_token_base_cost). Image tokens did not.

When input_cost_per_image_token is missing, _calculate_input_cost fell back
to looking up input_cost_per_token from model_info via calculate_cost_component,
which has no notion of _above_<X>_tokens and always returns the base rate.
A 250k all-image-token gemini-2.5-pro request was billed
250_000 * 1.25e-6 = $0.3125 instead of 250_000 * 2.5e-6 = $0.625.

Multiply by prompt_base_cost (the already-resolved tier rate) when the
image-specific key is absent. Mirrors the treatment text tokens get on line 556.

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -569,14 +569,20 @@ def _calculate_input_cost(
 
     ### IMAGE TOKEN COST
     if prompt_tokens_details["image_tokens"]:
-        # For image token costs:
-        # First check if input_cost_per_image_token is available. If not, default to generic input_cost_per_token.
-        image_token_cost_key = "input_cost_per_image_token"
-        if model_info.get(image_token_cost_key) is None:
-            image_token_cost_key = "input_cost_per_token"
-        prompt_cost += calculate_cost_component(
-            model_info, image_token_cost_key, prompt_tokens_details["image_tokens"]
-        )
+        # If input_cost_per_image_token is defined, use it directly.
+        # Otherwise charge at prompt_base_cost (the text rate already resolved
+        # for tiered keys like input_cost_per_token_above_200k_tokens), not the
+        # un-tiered input_cost_per_token from model_info.
+        if model_info.get("input_cost_per_image_token") is not None:
+            prompt_cost += calculate_cost_component(
+                model_info,
+                "input_cost_per_image_token",
+                prompt_tokens_details["image_tokens"],
+            )
+        else:
+            prompt_cost += (
+                float(prompt_tokens_details["image_tokens"]) * prompt_base_cost
+            )
 
     ### CACHE WRITING COST - Now uses tiered pricing
     if (

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py

@@ -298,6 +298,67 @@ def test_generic_cost_per_token_above_200k_tokens():
     )
 
 
+def test_input_image_tokens_use_custom_pricing_when_set():
+    """When input_cost_per_image_token IS defined, it must be used in preference
+    to prompt_base_cost. Covers the `if` branch of the image fallback fix."""
+    from unittest.mock import patch
+
+    mock_model_info = {
+        "input_cost_per_token": 1e-6,
+        "output_cost_per_token": 2e-6,
+        "input_cost_per_image_token": 3e-6,
+    }
+    usage = Usage(
+        prompt_tokens=1000,
+        completion_tokens=0,
+        total_tokens=1000,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            audio_tokens=None, cached_tokens=None, text_tokens=0, image_tokens=1000
+        ),
+    )
+    with patch(
+        "litellm.litellm_core_utils.llm_cost_calc.utils.get_model_info",
+        return_value=mock_model_info,
+    ):
+        prompt_cost, _ = generic_cost_per_token(
+            model="test-model", usage=usage, custom_llm_provider="gemini"
+        )
+    assert round(prompt_cost, 12) == round(1000 * 3e-6, 12)
+
+
+def test_image_tokens_above_200k_uses_tiered_rate():
+    """
+    gemini-2.5-pro charges 2x input_cost_per_token above 200k. The model_cost
+    entry has no input_cost_per_image_token, so image tokens above the
+    threshold must also use the above-200k rate, not the base rate.
+    """
+    model = "gemini-2.5-pro"
+    custom_llm_provider = "vertex_ai"
+    os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
+    litellm.model_cost = litellm.get_model_cost_map(url="")
+
+    model_cost_map = litellm.model_cost[model]
+    assert "input_cost_per_image_token" not in model_cost_map
+    above_200k_rate = model_cost_map["input_cost_per_token_above_200k_tokens"]
+
+    prompt_tokens = 250_000
+    usage = Usage(
+        prompt_tokens=prompt_tokens,
+        completion_tokens=0,
+        total_tokens=prompt_tokens,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            audio_tokens=None,
+            cached_tokens=None,
+            text_tokens=0,
+            image_tokens=prompt_tokens,
+        ),
+    )
+    prompt_cost, _ = generic_cost_per_token(
+        model=model, usage=usage, custom_llm_provider=custom_llm_provider
+    )
+    assert round(prompt_cost, 10) == round(prompt_tokens * above_200k_rate, 10)
+
+
 def test_generic_cost_per_token_gpt54_above_272k_tokens():
     """GPT-5.4/5.4-pro: prompts >272K input tokens priced at 2x input, 1.5x output."""
     model = "gpt-5.4"