fix(cost): bill cached realtime audio tokens at the audio cache-read rate

litellm/cost_calculator.py

@@ -97,6 +97,7 @@
 from litellm.llms.xai.cost_calculator import cost_per_token as xai_cost_per_token
 from litellm.responses.utils import ResponseAPILoggingUtils
 from litellm.types.agents import LiteLLMSendMessageResponse
+from litellm.types.llms.base import CachedTokensDetails
 from litellm.types.llms.openai import (
     HttpxBinaryResponseContent,
     ImageGenerationRequestQuality,
@@ -2381,6 +2382,46 @@ def _summable_prompt_token_fields(prompt_tokens_details: BaseModel) -> list[str]
     return [attr for attr in field_names if attr != "cache_creation_tokens"]
 
 
+def _combine_cached_tokens_details(
+    current: CachedTokensDetails | None, new: CachedTokensDetails
+) -> CachedTokensDetails:
+    def _sum_optional(current_value: int | None, new_value: int | None) -> int | None:
+        if current_value is None and new_value is None:
+            return None
+        return (current_value or 0) + (new_value or 0)
+
+    return CachedTokensDetails(
+        text_tokens=_sum_optional(current.text_tokens if current is not None else None, new.text_tokens),
+        audio_tokens=_sum_optional(current.audio_tokens if current is not None else None, new.audio_tokens),
+        image_tokens=_sum_optional(current.image_tokens if current is not None else None, new.image_tokens),
+    )
+
+
+def _combine_prompt_tokens_details(
+    current: PromptTokensDetailsWrapper | None, new: PromptTokensDetailsWrapper
+) -> PromptTokensDetailsWrapper:
+    base: Final = current if current is not None else PromptTokensDetailsWrapper()
+    base_values: Final = MappingProxyType(
+        {attr: getattr(base, attr) for attr in type(base).model_fields if hasattr(base, attr)}
+    )
+    summed: Final = MappingProxyType(
+        {
+            attr: (getattr(base, attr, 0) or 0) + (getattr(new, attr) or 0)
+            for attr in _summable_prompt_token_fields(new)
+            if hasattr(new, attr) and isinstance(getattr(new, attr) or 0, (int, float))
+        }
+    )
+    new_cached_tokens_details: Final = getattr(new, "cached_tokens_details", None)
+    cached_tokens_details: Final = (
+        _combine_cached_tokens_details(getattr(base, "cached_tokens_details", None), new_cached_tokens_details)
+        if isinstance(new_cached_tokens_details, CachedTokensDetails)
+        else getattr(base, "cached_tokens_details", None)
+    )
+    return PromptTokensDetailsWrapper(
+        **MappingProxyType({**base_values, **summed, "cached_tokens_details": cached_tokens_details})
+    )
+
+
 class BaseTokenUsageProcessor:
     @staticmethod
     def combine_usage_objects(usage_objects: list[Usage]) -> Usage:
@@ -2389,7 +2430,6 @@ def combine_usage_objects(usage_objects: list[Usage]) -> Usage:
         """
         from litellm.types.utils import (
             CompletionTokensDetailsWrapper,
-            PromptTokensDetailsWrapper,
             Usage,
         )
 
@@ -2408,27 +2448,10 @@ def combine_usage_objects(usage_objects: list[Usage]) -> Usage:
                         and isinstance(current_val, (int, float))
                     ):
                         setattr(combined, attr, current_val + new_val)
-            # Handle nested prompt_tokens_details
             if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details:
-                if not hasattr(combined, "prompt_tokens_details") or not combined.prompt_tokens_details:
-                    combined.prompt_tokens_details = PromptTokensDetailsWrapper()
-
-                # Check what keys exist in the model's prompt_tokens_details
-                # Access model_fields on the class, not the instance, to avoid Pydantic 2.11+ deprecation warnings
-                for attr in _summable_prompt_token_fields(usage.prompt_tokens_details):
-                    if (
-                        hasattr(usage.prompt_tokens_details, attr)
-                        and not attr.startswith("_")
-                        and not callable(_attribute_value(usage.prompt_tokens_details, attr))
-                    ):
-                        current_val = getattr(combined.prompt_tokens_details, attr, 0) or 0
-                        new_val = getattr(usage.prompt_tokens_details, attr, 0) or 0
-                        if new_val is not None and isinstance(new_val, (int, float)):
-                            setattr(
-                                combined.prompt_tokens_details,
-                                attr,
-                                current_val + new_val,
-                            )
+                combined.prompt_tokens_details = _combine_prompt_tokens_details(
+                    getattr(combined, "prompt_tokens_details", None), usage.prompt_tokens_details
+                )
 
             # Handle nested completion_tokens_details
             if hasattr(usage, "completion_tokens_details") and usage.completion_tokens_details:

litellm/litellm_core_utils/llm_cost_calc/utils.py

@@ -9,6 +9,8 @@
 from typing import Any, Final, Literal, TypedDict, cast
 from zoneinfo import ZoneInfo, ZoneInfoNotFoundError
 
+from typing_extensions import ReadOnly
+
 import litellm
 from litellm._internal_context import current_billing_time
 from litellm._logging import verbose_logger
@@ -772,6 +774,7 @@ def calculate_cache_writing_cost(
 
 class PromptTokensDetailsResult(TypedDict):
     cache_hit_tokens: int
+    cache_hit_audio_tokens: ReadOnly[int]
     cache_creation_tokens: int
     cache_creation_token_details: CacheCreationTokenDetails | None
     text_tokens: int
@@ -802,12 +805,34 @@ def parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult:
         )
         or None
     )
-    text_tokens: Final = (
-        cast(int | None, getattr(usage.prompt_tokens_details, "text_tokens", None))
-        or 0  # default to prompt tokens, if this field is not set
+    cached_tokens_details: Final = getattr(usage.prompt_tokens_details, "cached_tokens_details", None)
+    cached_audio_tokens: Final = min(
+        _get_token_detail_value(cached_tokens_details, "audio_tokens") or 0, cache_hit_tokens
+    )
+    cached_text_tokens: Final = min(
+        _get_token_detail_value(cached_tokens_details, "text_tokens") or 0,
+        cache_hit_tokens - cached_audio_tokens,
+    )
+    cached_image_tokens: Final = min(
+        _get_token_detail_value(cached_tokens_details, "image_tokens") or 0,
+        cache_hit_tokens - cached_audio_tokens - cached_text_tokens,
+    )
+    text_tokens: Final = max(
+        (
+            cast(int | None, getattr(usage.prompt_tokens_details, "text_tokens", None))
+            or 0  # default to prompt tokens, if this field is not set
+        )
+        - cached_text_tokens,
+        0,
+    )
+    audio_tokens: Final = max(
+        (cast(int | None, getattr(usage.prompt_tokens_details, "audio_tokens", 0)) or 0) - cached_audio_tokens,
+        0,
+    )
+    image_tokens: Final = max(
+        (cast(int | None, getattr(usage.prompt_tokens_details, "image_tokens", 0)) or 0) - cached_image_tokens,
+        0,
     )
-    audio_tokens: Final = cast(int | None, getattr(usage.prompt_tokens_details, "audio_tokens", 0)) or 0
-    image_tokens: Final = cast(int | None, getattr(usage.prompt_tokens_details, "image_tokens", 0)) or 0
     video_tokens: Final = _coerce_token_count(getattr(usage.prompt_tokens_details, "video_tokens", 0))
     character_count: Final = (
         cast(
@@ -835,6 +860,7 @@ def parse_prompt_tokens_details(usage: Usage) -> PromptTokensDetailsResult:
 
     return PromptTokensDetailsResult(
         cache_hit_tokens=cache_hit_tokens,
+        cache_hit_audio_tokens=cached_audio_tokens,
         cache_creation_tokens=cache_creation_tokens,
         cache_creation_token_details=cache_creation_token_details,
         text_tokens=text_tokens,
@@ -918,7 +944,16 @@ def _calculate_input_cost(
     prompt_cost = float(prompt_tokens_details["text_tokens"]) * prompt_base_cost
 
     ### CACHE READ COST - Now uses tiered pricing
-    prompt_cost += float(prompt_tokens_details["cache_hit_tokens"]) * cache_read_cost
+    cache_hit_audio_tokens: Final = prompt_tokens_details["cache_hit_audio_tokens"]
+    audio_cache_read_rate: Final = _get_cost_per_unit(
+        model_info,
+        _get_service_tier_cost_key("cache_read_input_audio_token_cost", service_tier),
+        None,
+    )
+    prompt_cost += float(prompt_tokens_details["cache_hit_tokens"] - cache_hit_audio_tokens) * cache_read_cost
+    prompt_cost += float(cache_hit_audio_tokens) * (
+        audio_cache_read_rate if audio_cache_read_rate is not None else cache_read_cost
+    )
 
     ### AUDIO COST
     if prompt_tokens_details["audio_tokens"]:
@@ -1149,6 +1184,7 @@ def generic_cost_per_token(
     ### PROCESSING COST
     prompt_tokens_details = PromptTokensDetailsResult(
         cache_hit_tokens=0,
+        cache_hit_audio_tokens=0,
         cache_creation_tokens=0,
         cache_creation_token_details=None,
         text_tokens=usage.prompt_tokens,
@@ -1319,6 +1355,7 @@ class BilledTokenRates:
     input_cost_per_token: float
     output_cost_per_token: float
     cache_read_input_token_cost: float
+    cache_read_input_audio_token_cost: float
     cache_creation_input_token_cost: float
     cache_creation_input_token_cost_above_1hr: float
     output_cost_per_reasoning_token: float
@@ -1330,6 +1367,7 @@ def scaled(self, multiplier: float) -> "BilledTokenRates":
             input_cost_per_token=self.input_cost_per_token * multiplier,
             output_cost_per_token=self.output_cost_per_token * multiplier,
             cache_read_input_token_cost=self.cache_read_input_token_cost * multiplier,
+            cache_read_input_audio_token_cost=self.cache_read_input_audio_token_cost * multiplier,
             cache_creation_input_token_cost=self.cache_creation_input_token_cost * multiplier,
             cache_creation_input_token_cost_above_1hr=self.cache_creation_input_token_cost_above_1hr * multiplier,
             output_cost_per_reasoning_token=self.output_cost_per_reasoning_token * multiplier,
@@ -1353,15 +1391,16 @@ def _reasoning_token_count(usage: Usage) -> int:
     return parsed or _coerce_token_count(getattr(usage, "reasoning_tokens", 0))
 
 
-def _cache_token_counts(usage: Usage) -> tuple[int, int, CacheCreationTokenDetails | None]:
-    """(cache read tokens, cache creation tokens, cache creation details): read from prompt_tokens_details
-    first, then the private top-level counters the Usage constructor mirrors cache tokens onto for
-    providers/callers that bypass the details."""
+def _cache_token_counts(usage: Usage) -> tuple[int, int, int, CacheCreationTokenDetails | None]:
+    """(cache read tokens, cached audio tokens, cache creation tokens, cache creation details): read from
+    prompt_tokens_details first, then the private top-level counters the Usage constructor mirrors cache
+    tokens onto for providers/callers that bypass the details."""
     parsed: Final = parse_prompt_tokens_details(usage) if usage.prompt_tokens_details is not None else None
     parsed_read: Final = parsed["cache_hit_tokens"] if parsed is not None else 0
     parsed_creation: Final = parsed["cache_creation_tokens"] if parsed is not None else 0
     return (
         parsed_read or _coerce_token_count(getattr(usage, "_cache_read_input_tokens", 0)),
+        parsed["cache_hit_audio_tokens"] if parsed is not None else 0,
         parsed_creation or _coerce_token_count(getattr(usage, "_cache_creation_input_tokens", 0)),
         parsed["cache_creation_token_details"] if parsed is not None else None,
     )
@@ -1372,11 +1411,13 @@ def _custom_pricing_rates(custom_cost_per_token: CostPerToken) -> BilledTokenRat
     cache rates (else the input rate) and reasoning at the output rate, as _cost_per_token_custom_pricing_helper does."""
     input_rate: Final = custom_cost_per_token["input_cost_per_token"]
     output_rate: Final = custom_cost_per_token["output_cost_per_token"]
+    cache_read_rate: Final = custom_cost_per_token.get("cache_read_input_token_cost", input_rate)
     cache_creation_rate: Final = custom_cost_per_token.get("cache_creation_input_token_cost", input_rate)
     return BilledTokenRates(
         input_cost_per_token=input_rate,
         output_cost_per_token=output_rate,
-        cache_read_input_token_cost=custom_cost_per_token.get("cache_read_input_token_cost", input_rate),
+        cache_read_input_token_cost=cache_read_rate,
+        cache_read_input_audio_token_cost=cache_read_rate,
         cache_creation_input_token_cost=cache_creation_rate,
         cache_creation_input_token_cost_above_1hr=cache_creation_rate,
         output_cost_per_reasoning_token=output_rate,
@@ -1413,6 +1454,11 @@ def _cost_map_billed_rates(
         completion_base_cost=completion_base_cost,
         current_time=billing_time,
     )
+    audio_cache_read_rate: Final = _get_cost_per_unit(
+        model_info,
+        _get_service_tier_cost_key("cache_read_input_audio_token_cost", service_tier),
+        None,
+    )
     multiplier: Final = (
         _get_regional_uplift_multiplier(model_info, data_residency)
         * get_vertex_regional_endpoint_uplift(model_info, vertex_location)
@@ -1422,6 +1468,9 @@ def _cost_map_billed_rates(
         input_cost_per_token=prompt_base_cost,
         output_cost_per_token=completion_base_cost,
         cache_read_input_token_cost=cache_read_cost_rate,
+        cache_read_input_audio_token_cost=(
+            audio_cache_read_rate if audio_cache_read_rate is not None else cache_read_cost_rate
+        ),
         cache_creation_input_token_cost=cache_creation_cost_rate,
         cache_creation_input_token_cost_above_1hr=cache_creation_cost_above_1hr_rate,
         output_cost_per_reasoning_token=reasoning_rate,
@@ -1494,7 +1543,9 @@ def get_token_type_cost_breakdown(
     if rates is None:
         return TokenTypeCostBreakdown(0.0, 0.0, 0.0)
 
-    cache_read_tokens, cache_creation_tokens, cache_creation_token_details = _cache_token_counts(usage)
+    cache_read_tokens, cached_audio_tokens, cache_creation_tokens, cache_creation_token_details = _cache_token_counts(
+        usage
+    )
     cache_creation_cost: Final = (
         float(cache_creation_tokens) * rates.cache_creation_input_token_cost
         if custom_cost_per_token is not None
@@ -1507,7 +1558,10 @@ def get_token_type_cost_breakdown(
     )
     return TokenTypeCostBreakdown(
         reasoning_cost=float(_reasoning_token_count(usage)) * rates.output_cost_per_reasoning_token,
-        cache_read_cost=float(cache_read_tokens) * rates.cache_read_input_token_cost,
+        cache_read_cost=(
+            float(cache_read_tokens - cached_audio_tokens) * rates.cache_read_input_token_cost
+            + float(cached_audio_tokens) * rates.cache_read_input_audio_token_cost
+        ),
         cache_creation_cost=cache_creation_cost,
         rates=rates,
     )

litellm/model_prices_and_context_window_backup.json

@@ -5513,7 +5513,8 @@
     },
     "azure/gpt-realtime-2025-08-28": {
         "cache_creation_input_audio_token_cost": 4e-06,
-        "cache_read_input_token_cost": 4e-06,
+        "cache_read_input_audio_token_cost": 4e-07,
+        "cache_read_input_token_cost": 4e-07,
         "deprecation_date": "2027-03-02",
         "input_cost_per_audio_token": 3.2e-05,
         "input_cost_per_image_token": 5e-06,
@@ -5546,7 +5547,8 @@
     },
     "azure/gpt-realtime-1.5-2026-02-23": {
         "cache_creation_input_audio_token_cost": 4e-06,
-        "cache_read_input_token_cost": 4e-06,
+        "cache_read_input_audio_token_cost": 4e-07,
+        "cache_read_input_token_cost": 4e-07,
         "deprecation_date": "2027-08-24",
         "input_cost_per_audio_token": 3.2e-05,
         "input_cost_per_image_token": 5e-06,
@@ -5683,6 +5685,7 @@
     },
     "azure/gpt-realtime-mini": {
         "cache_creation_input_audio_token_cost": 3e-07,
+        "cache_read_input_audio_token_cost": 3e-07,
         "cache_read_input_token_cost": 6e-08,
         "input_cost_per_audio_token": 1e-05,
         "input_cost_per_image_token": 8e-07,
@@ -5715,6 +5718,7 @@
     },
     "azure/gpt-realtime-mini-2025-10-06": {
         "cache_creation_input_audio_token_cost": 3e-07,
+        "cache_read_input_audio_token_cost": 3e-07,
         "cache_read_input_token_cost": 6e-08,
         "input_cost_per_audio_token": 1e-05,
         "input_cost_per_image_token": 8e-07,
@@ -32678,6 +32682,7 @@
     },
     "gpt-realtime": {
         "cache_creation_input_audio_token_cost": 4e-07,
+        "cache_read_input_audio_token_cost": 4e-07,
         "cache_read_input_token_cost": 4e-07,
         "deprecation_date": "2027-01-20",
         "input_cost_per_audio_token": 3.2e-05,
@@ -32711,6 +32716,7 @@
     },
     "gpt-realtime-1.5": {
         "cache_creation_input_audio_token_cost": 4e-07,
+        "cache_read_input_audio_token_cost": 4e-07,
         "cache_read_input_token_cost": 4e-07,
         "input_cost_per_audio_token": 3.2e-05,
         "input_cost_per_image_token": 5e-06,
@@ -32847,6 +32853,7 @@
     "gpt-realtime-mini": {
         "cache_creation_input_audio_token_cost": 3e-07,
         "cache_read_input_audio_token_cost": 3e-07,
+        "cache_read_input_token_cost": 6e-08,
         "deprecation_date": "2027-01-20",
         "input_cost_per_audio_token": 1e-05,
         "input_cost_per_token": 6e-07,
@@ -32878,6 +32885,7 @@
     },
     "gpt-realtime-2025-08-28": {
         "cache_creation_input_audio_token_cost": 4e-07,
+        "cache_read_input_audio_token_cost": 4e-07,
         "cache_read_input_token_cost": 4e-07,
         "deprecation_date": "2027-01-20",
         "input_cost_per_audio_token": 3.2e-05,