fix(cost): avoid double billing Gemini embedding modalities

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -707,6 +707,24 @@ def calculate_cost_component(model_info: ModelInfo, cost_key: str, usage_value:
     return 0.0
 
 
+def _calculate_modality_input_cost(
+    model_info: ModelInfo,
+    tokens: int,
+    token_cost_key: str,
+    units: float,
+    unit_cost_key: str,
+    token_fallback_key: str | None = None,
+    cached_tokens: int = 0,
+) -> float:
+    token_rate: Final = _get_cost_per_unit(model_info, token_cost_key, None)
+    if token_rate is not None and tokens + cached_tokens > 0:
+        return tokens * token_rate
+    unit_rate: Final = _get_cost_per_unit(model_info, unit_cost_key, None)
+    if unit_rate is not None and units > 0:
+        return units * unit_rate
+    return calculate_cost_component(model_info, token_fallback_key or token_cost_key, tokens)
+
+
 def _get_cost_per_unit(model_info: ModelInfo, cost_key: str, default_value: float | None = 0.0) -> float | None:
     # Sometimes the cost per unit is a string (e.g.: If a value like "3e-7" was read from the config.yaml)
     cost_per_unit: Final = model_info.get(cost_key)
@@ -955,26 +973,30 @@ def _calculate_input_cost(
         audio_cache_read_rate if audio_cache_read_rate is not None else cache_read_cost
     )
 
-    ### AUDIO COST
-    if prompt_tokens_details["audio_tokens"]:
-        audio_cost_key: Final = _get_service_tier_cost_key("input_cost_per_audio_token", service_tier)
-        prompt_cost += calculate_cost_component(model_info, audio_cost_key, prompt_tokens_details["audio_tokens"])
-
-    ### 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"])
-
-    ### VIDEO TOKEN COST
-    if prompt_tokens_details["video_tokens"]:
-        video_token_cost_key = "input_cost_per_video_token"
-        if model_info.get(video_token_cost_key) is None:
-            video_token_cost_key = "input_cost_per_token"
-        prompt_cost += calculate_cost_component(model_info, video_token_cost_key, prompt_tokens_details["video_tokens"])
+    prompt_cost += _calculate_modality_input_cost(
+        model_info,
+        prompt_tokens_details["audio_tokens"],
+        _get_service_tier_cost_key("input_cost_per_audio_token", service_tier),
+        prompt_tokens_details["audio_length_seconds"],
+        "input_cost_per_audio_per_second",
+        cached_tokens=cache_hit_audio_tokens,
+    )
+    prompt_cost += _calculate_modality_input_cost(
+        model_info,
+        prompt_tokens_details["image_tokens"],
+        "input_cost_per_image_token",
+        prompt_tokens_details["image_count"],
+        "input_cost_per_image",
+        token_fallback_key="input_cost_per_token",
+    )
+    prompt_cost += _calculate_modality_input_cost(
+        model_info,
+        prompt_tokens_details["video_tokens"],
+        "input_cost_per_video_token",
+        prompt_tokens_details["video_length_seconds"],
+        "input_cost_per_video_per_second",
+        token_fallback_key="input_cost_per_token",
+    )
 
     ### CACHE WRITING COST - Now uses tiered pricing
     if (
@@ -996,28 +1018,6 @@ def _calculate_input_cost(
             prompt_tokens_details["character_count"],
         )
 
-    ### IMAGE COUNT COST
-    if prompt_tokens_details["image_count"]:
-        prompt_cost += calculate_cost_component(
-            model_info, "input_cost_per_image", prompt_tokens_details["image_count"]
-        )
-
-    ### VIDEO LENGTH COST
-    if prompt_tokens_details["video_length_seconds"]:
-        prompt_cost += calculate_cost_component(
-            model_info,
-            "input_cost_per_video_per_second",
-            prompt_tokens_details["video_length_seconds"],
-        )
-
-    ### AUDIO LENGTH COST
-    if prompt_tokens_details["audio_length_seconds"]:
-        prompt_cost += calculate_cost_component(
-            model_info,
-            "input_cost_per_audio_per_second",
-            prompt_tokens_details["audio_length_seconds"],
-        )
-
     if prompt_tokens_details["query_count"]:
         prompt_cost += calculate_cost_component(
             model_info, "input_cost_per_query", prompt_tokens_details["query_count"]

litellm/llms/vertex_ai/gemini_embeddings/batch_embed_content_transformation.py

@@ -374,25 +374,21 @@ def _usage_from_embed_content_response(
     details: Final[Sequence[PromptTokensDetails]] = usage_metadata.get("promptTokensDetails") or ()
     text_tokens: Final = _tokens_for_modality(details, "TEXT")
     audio_tokens: Final = _tokens_for_modality(details, "AUDIO")
+    image_tokens: Final = _tokens_for_modality(details, "IMAGE")
     video_tokens: Final = _tokens_for_modality(details, "VIDEO")
     image_count: Final = _count_input_images(input, resolved_files)
 
     video_length_seconds: Final = video_tokens / _VIDEO_TOKENS_PER_SECOND if video_tokens > 0 else 0.0
     audio_length_seconds: Final = audio_tokens / _AUDIO_TOKENS_PER_SECOND if audio_tokens > 0 else 0.0
 
-    # generic_cost_per_token rewrites text_tokens to the full prompt minus
-    # other modalities when both text_tokens and image_count are zero. For
-    # video, that misallocates video tokens to text; a 1-token floor sidesteps
-    # the rewrite and keeps billing on input_cost_per_video_per_second.
-    needs_video_text_floor: Final = video_length_seconds > 0 and text_tokens == 0 and image_count == 0
-    resolved_text_tokens: Final = 1 if needs_video_text_floor else text_tokens
-
     return Usage(
         prompt_tokens=prompt_tokens,
         total_tokens=total_tokens,
         prompt_tokens_details=PromptTokensDetailsWrapper(
-            text_tokens=resolved_text_tokens,
+            text_tokens=text_tokens,
             audio_tokens=audio_tokens,
+            image_tokens=image_tokens,
+            video_tokens=video_tokens,
             image_count=image_count,
             video_length_seconds=video_length_seconds,
             audio_length_seconds=audio_length_seconds,

litellm/model_prices_and_context_window_backup.json

@@ -25602,9 +25602,12 @@
     },
     "gemini-embedding-2-preview": {
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai-embedding-models",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25622,6 +25625,7 @@
         "input_cost_per_token": 2e-07,
         "input_cost_per_token_batches": 1e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai-embedding-models",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25634,9 +25638,12 @@
     },
     "vertex_ai/gemini-embedding-2-preview": {
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25655,6 +25662,7 @@
         "input_cost_per_token": 2e-07,
         "input_cost_per_token_batches": 1e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25694,9 +25702,12 @@
     "gemini/gemini-embedding-2-preview": {
         "deprecation_date": "2026-08-10",
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "gemini",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25710,9 +25721,12 @@
     },
     "gemini/gemini-embedding-2": {
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "gemini",
         "max_input_tokens": 8192,
         "max_tokens": 8192,

model_prices_and_context_window.json

@@ -25602,9 +25602,12 @@
     },
     "gemini-embedding-2-preview": {
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai-embedding-models",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25622,6 +25625,7 @@
         "input_cost_per_token": 2e-07,
         "input_cost_per_token_batches": 1e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai-embedding-models",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25634,9 +25638,12 @@
     },
     "vertex_ai/gemini-embedding-2-preview": {
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25655,6 +25662,7 @@
         "input_cost_per_token": 2e-07,
         "input_cost_per_token_batches": 1e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "vertex_ai",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25694,9 +25702,12 @@
     "gemini/gemini-embedding-2-preview": {
         "deprecation_date": "2026-08-10",
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "gemini",
         "max_input_tokens": 8192,
         "max_tokens": 8192,
@@ -25710,9 +25721,12 @@
     },
     "gemini/gemini-embedding-2": {
         "input_cost_per_audio_per_second": 0.00016,
+        "input_cost_per_audio_token": 6.5e-06,
         "input_cost_per_image": 0.00012,
+        "input_cost_per_image_token": 4.5e-07,
         "input_cost_per_token": 2e-07,
         "input_cost_per_video_per_second": 0.00079,
+        "input_cost_per_video_token": 1.2e-05,
         "litellm_provider": "gemini",
         "max_input_tokens": 8192,
         "max_tokens": 8192,

model_prices_and_context_window.schema.json

@@ -375,6 +375,10 @@
           "minimum": 0,
           "description": "Rate applied once the prompt exceeds the token threshold in the field name."
         },
+        "input_cost_per_video_token": {
+          "type": "number",
+          "minimum": 0
+        },
         "input_dbu_cost_per_token": {
           "type": "number",
           "minimum": 0

tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py

@@ -1,5 +1,6 @@
 import json
 from datetime import datetime, timezone
+from typing import Final
 
 import pytest
 from fastapi.testclient import TestClient
@@ -5334,3 +5335,44 @@ def test_realtime_models_bill_cached_text_and_audio_at_their_cache_read_rates(
 
     prompt_cost, _ = generic_cost_per_token(model=model, usage=usage, custom_llm_provider=custom_llm_provider)
     assert prompt_cost == pytest.approx(expected_prompt_cost)
+
+
+@pytest.mark.parametrize("tokens,cached_audio,token_rate,unit_rate,expected", [
+    (10, 0, 0.1, 7.0, 3.0),
+    (10, 0, 0.0, 7.0, 0.0),
+    (10, 0, None, 7.0, 42.0),
+    (0, 0, 0.1, 7.0, 42.0),
+    (0, 10, 0.1, 7.0, 28.0),
+    (10, 0, None, None, 0.2),
+    (10, 0, None, 0.0, 0.0),
+])
+def test_modality_tokens_and_physical_units_are_alternative_prices(
+    tokens: int, cached_audio: int, token_rate: float | None,
+    unit_rate: float | None, expected: float,
+) -> None:
+    info: Final[ModelInfo] = {
+        "input_cost_per_token": 0.01,
+        "input_cost_per_audio_token": token_rate,
+        "input_cost_per_image_token": token_rate,
+        "input_cost_per_video_token": token_rate,
+        "input_cost_per_audio_per_second": unit_rate,
+        "input_cost_per_image": unit_rate,
+        "input_cost_per_video_per_second": unit_rate,
+        "cache_read_input_token_cost": 0.0,
+    }
+    usage: Final = Usage(
+        prompt_tokens=3 * tokens + cached_audio,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=0, audio_tokens=tokens + cached_audio,
+            image_tokens=tokens, video_tokens=tokens,
+            cached_tokens=cached_audio,
+            cached_tokens_details={"audio_tokens": cached_audio},
+            image_count=2, audio_length_seconds=2.0,
+            video_length_seconds=2.0,
+        ),
+    )
+    prompt_cost, completion_cost = generic_cost_per_token(
+        "modality-unit-test", usage, "vertex_ai", model_info=info,
+    )
+    assert prompt_cost == pytest.approx(expected)
+    assert completion_cost == 0