Include multimodal tokens in tiered pricing threshold

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -616,6 +616,7 @@ def _get_effective_prompt_tokens_for_tiered_pricing(usage: Usage) -> float:
     them separate. When prompt_tokens_details is present, prefer the category
     fields so the threshold check can avoid double-counting cache tokens.
     """
+    prompt_tokens = float(getattr(usage, "prompt_tokens", 0) or 0)
     if usage.prompt_tokens_details is not None:
         details = usage.prompt_tokens_details
         raw_text_tokens = getattr(details, "text_tokens", None)
@@ -626,17 +627,29 @@ def _get_effective_prompt_tokens_for_tiered_pricing(usage: Usage) -> float:
             # If prompt_tokens already covers the cache detail total, use it as
             # the rolled-up input total. Otherwise cache tokens were reported
             # outside prompt_tokens and must be added for the tier threshold.
-            prompt_tokens = float(getattr(usage, "prompt_tokens", 0) or 0)
             cache_detail_tokens = cached_tokens + cache_creation
             if cache_detail_tokens > 0 and prompt_tokens < cache_detail_tokens:
                 return prompt_tokens + cache_detail_tokens
             return prompt_tokens
         text_tokens = float(raw_text_tokens or 0)
-        return text_tokens + cached_tokens + cache_creation
+        audio_tokens = float(getattr(details, "audio_tokens", 0) or 0)
+        image_tokens = float(getattr(details, "image_tokens", 0) or 0)
+        video_tokens = float(getattr(details, "video_tokens", 0) or 0)
+        detail_total = (
+            text_tokens
+            + audio_tokens
+            + image_tokens
+            + video_tokens
+            + cached_tokens
+            + cache_creation
+        )
+        cache_detail_tokens = cached_tokens + cache_creation
+        if cache_detail_tokens > 0 and detail_total > prompt_tokens:
+            return prompt_tokens
+        return max(prompt_tokens, detail_total)
 
     # No prompt_tokens_details. Add explicit cache fields only if they are
     # not already rolled into prompt_tokens (determined by their presence).
-    prompt_tokens = float(getattr(usage, "prompt_tokens", 0) or 0)
     cache_read_tokens = float(getattr(usage, "cache_read_input_tokens", 0) or 0)
     cache_creation_tokens = float(getattr(usage, "cache_creation_input_tokens", 0) or 0)
     return prompt_tokens + cache_read_tokens + cache_creation_tokens
@@ -1140,8 +1153,11 @@ def _calculate_input_cost(
         # 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"])
+            prompt_cost += float(prompt_tokens_details["image_tokens"]) * prompt_base_cost
+        else:
+            prompt_cost += calculate_cost_component(
+                model_info, image_token_cost_key, prompt_tokens_details["image_tokens"]
+            )
 
     ### VIDEO TOKEN COST
     if prompt_tokens_details["video_tokens"] and not (

tests/test_litellm/test_cost_calculator.py

@@ -2661,6 +2661,63 @@ def test_custom_pricing_does_not_double_count_cached_tokens_when_text_tokens_mis
         litellm.model_cost.pop(f"anthropic/{model_id}", None)
 
 
+def test_custom_pricing_uses_tiered_rate_when_multimodal_details_cross_threshold():
+    """
+    Multimodal prompt detail tokens should count toward tier thresholds even
+    when text_tokens is present.
+    """
+
+    model_id = "tiered-custom-pricing-multimodal-details-test"
+    model_info = {
+        "litellm_provider": "openai",
+        "mode": "chat",
+        "input_cost_per_token": 0.000005,
+        "output_cost_per_token": 0.000025,
+        "input_cost_per_token_above_200k_tokens": 0.00001,
+        "output_cost_per_token_above_200k_tokens": 0.0000375,
+    }
+    text_tokens = 150_000
+    image_tokens = 60_001
+
+    litellm.register_model(model_cost={model_id: model_info})
+    try:
+        response = ModelResponse(
+            id="test-id",
+            created=1234567890,
+            model=f"openai/{model_id}",
+            object="chat.completion",
+            choices=[],
+            usage=Usage(
+                prompt_tokens=text_tokens + image_tokens,
+                completion_tokens=0,
+                total_tokens=text_tokens + image_tokens,
+                prompt_tokens_details=PromptTokensDetailsWrapper(
+                    text_tokens=text_tokens,
+                    image_tokens=image_tokens,
+                ),
+            ),
+        )
+
+        cost = completion_cost(
+            completion_response=response,
+            model=f"openai/{model_id}",
+            custom_llm_provider="openai",
+            custom_pricing=True,
+            router_model_id=model_id,
+        )
+
+        expected_cost = (
+            text_tokens + image_tokens
+        ) * model_info["input_cost_per_token_above_200k_tokens"]
+        base_cost = (text_tokens + image_tokens) * model_info["input_cost_per_token"]
+
+        assert cost == pytest.approx(expected_cost)
+        assert cost != pytest.approx(base_cost)
+    finally:
+        litellm.model_cost.pop(model_id, None)
+        litellm.model_cost.pop(f"openai/{model_id}", None)
+
+
 def test_extract_cache_read_tokens_anthropic_top_level():
     from litellm.proxy.spend_tracking.savings import extract_cache_read_tokens as _extract_cache_read_tokens