diff --git a/litellm/cost_calculator.py b/litellm/cost_calculator.py
index 440d97d13be..f5319776213 100644
--- a/litellm/cost_calculator.py
+++ b/litellm/cost_calculator.py
@@ -97,6 +97,7 @@ from litellm.llms.vertex_ai.cost_calculator import cost_router as google_cost_ro
 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 @@ class BaseTokenUsageProcessor:
         """
         from litellm.types.utils import (
             CompletionTokensDetailsWrapper,
-            PromptTokensDetailsWrapper,
             Usage,
         )
 
@@ -2408,27 +2448,10 @@ class BaseTokenUsageProcessor:
                         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:
diff --git a/litellm/litellm_core_utils/llm_cost_calc/utils.py b/litellm/litellm_core_utils/llm_cost_calc/utils.py
index e5977ca4156..8fc428b38ae 100644
--- a/litellm/litellm_core_utils/llm_cost_calc/utils.py
+++ b/litellm/litellm_core_utils/llm_cost_calc/utils.py
@@ -9,6 +9,8 @@ from types import MappingProxyType
 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 @@ class 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,
     )
diff --git a/litellm/model_prices_and_context_window_backup.json b/litellm/model_prices_and_context_window_backup.json
index 2220d0e1fe5..6d63fd8e3a9 100644
--- a/litellm/model_prices_and_context_window_backup.json
+++ b/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,
diff --git a/litellm/responses/litellm_completion_transformation/transformation.py b/litellm/responses/litellm_completion_transformation/transformation.py
index e8aacac9e67..64324c6cad8 100644
--- a/litellm/responses/litellm_completion_transformation/transformation.py
+++ b/litellm/responses/litellm_completion_transformation/transformation.py
@@ -43,6 +43,7 @@ from litellm.litellm_core_utils.litellm_logging import Logging as LiteLLMLogging
 from litellm.responses.litellm_completion_transformation.session_handler import (
     ResponsesSessionHandler,
 )
+from litellm.types.llms.base import CachedTokensDetails
 from litellm.types.llms.openai import (
     AllMessageValues,
     ChatCompletionAssistantMessage,
@@ -2816,27 +2817,24 @@ class LiteLLMCompletionResponsesConfig:
         # Translate prompt_tokens_details to input_tokens_details
         if hasattr(usage, "prompt_tokens_details") and usage.prompt_tokens_details is not None:
             prompt_details: Final = usage.prompt_tokens_details
-            input_details_dict: Final[dict[str, int]] = {}
-
-            if hasattr(prompt_details, "cached_tokens") and prompt_details.cached_tokens is not None:
-                input_details_dict["cached_tokens"] = prompt_details.cached_tokens
-            else:
-                input_details_dict["cached_tokens"] = 0
-
-            if hasattr(prompt_details, "text_tokens") and prompt_details.text_tokens is not None:
-                input_details_dict["text_tokens"] = prompt_details.text_tokens
-
-            if hasattr(prompt_details, "audio_tokens") and prompt_details.audio_tokens is not None:
-                input_details_dict["audio_tokens"] = prompt_details.audio_tokens
-
-            cache_write_tokens = getattr(prompt_details, "cache_write_tokens", None) or getattr(
+            cached_tokens_details: Final = getattr(prompt_details, "cached_tokens_details", None)
+            cache_write_tokens: Final = getattr(prompt_details, "cache_write_tokens", None) or getattr(
                 prompt_details, "cache_creation_tokens", None
             )
-            if cache_write_tokens is not None:
-                input_details_dict["cache_write_tokens"] = cache_write_tokens
-
-            if input_details_dict:
-                response_usage.input_tokens_details = InputTokensDetails(**input_details_dict)
+            cache_write_extra: Final[Mapping[str, int]] = (
+                MappingProxyType({"cache_write_tokens": cache_write_tokens})
+                if cache_write_tokens is not None
+                else MappingProxyType({})
+            )
+            response_usage.input_tokens_details = InputTokensDetails(
+                cached_tokens=prompt_details.cached_tokens if prompt_details.cached_tokens is not None else 0,
+                text_tokens=prompt_details.text_tokens,
+                audio_tokens=prompt_details.audio_tokens,
+                cached_tokens_details=(
+                    cached_tokens_details if isinstance(cached_tokens_details, CachedTokensDetails) else None
+                ),
+                **cache_write_extra,
+            )
 
         # Translate completion_tokens_details to output_tokens_details
         if hasattr(usage, "completion_tokens_details") and usage.completion_tokens_details is not None:
diff --git a/litellm/responses/utils.py b/litellm/responses/utils.py
index 599e978df6a..d63e3ddf0aa 100644
--- a/litellm/responses/utils.py
+++ b/litellm/responses/utils.py
@@ -1179,6 +1179,9 @@ class ResponseAPILoggingUtils:
                     audio_tokens=getattr(response_api_usage.input_tokens_details, "audio_tokens", None),
                     text_tokens=getattr(response_api_usage.input_tokens_details, "text_tokens", None),
                     image_tokens=getattr(response_api_usage.input_tokens_details, "image_tokens", None),
+                    cached_tokens_details=getattr(
+                        response_api_usage.input_tokens_details, "cached_tokens_details", None
+                    ),
                     cache_write_tokens=getattr(response_api_usage.input_tokens_details, "cache_write_tokens", None),
                 )
         completion_tokens_details: CompletionTokensDetailsWrapper | None = None
diff --git a/litellm/types/llms/base.py b/litellm/types/llms/base.py
index f09727ad92b..938aa8064c9 100644
--- a/litellm/types/llms/base.py
+++ b/litellm/types/llms/base.py
@@ -75,3 +75,9 @@ class HiddenParams(OpenAIObject):
         data: Final = super().model_dump(**kwargs)
         data["_response_ms"] = self._response_ms
         return data
+
+
+class CachedTokensDetails(BaseModel):
+    text_tokens: int | None = None
+    audio_tokens: int | None = None
+    image_tokens: int | None = None
diff --git a/litellm/types/llms/openai.py b/litellm/types/llms/openai.py
index dfafe27e0a1..e3eac9b9205 100644
--- a/litellm/types/llms/openai.py
+++ b/litellm/types/llms/openai.py
@@ -91,6 +91,8 @@ from litellm.types.responses.main import (
     OutputImageGenerationCall,
 )
 
+from .base import CachedTokensDetails
+
 FileContent = IO[bytes] | bytes | PathLike
 
 FileTypes = (
@@ -1288,6 +1290,7 @@ class OutputTokensDetails(BaseLiteLLMOpenAIResponseObject):
 class InputTokensDetails(BaseLiteLLMOpenAIResponseObject):
     audio_tokens: int | None = None
     cached_tokens: int = 0
+    cached_tokens_details: CachedTokensDetails | None = None
     text_tokens: int | None = None
 
     model_config = {"extra": "allow"}
@@ -2254,10 +2257,17 @@ class OpenAIRealtimeInputAudioTranscriptionCompleted(TypedDict):
     usage: NotRequired[ReadOnly[Mapping[str, object]]]
 
 
+class OpenAIRealtimeCachedTokensDetails(TypedDict, total=False):
+    text_tokens: ReadOnly[int]
+    audio_tokens: ReadOnly[int]
+    image_tokens: ReadOnly[int]
+
+
 class OpenAIRealtimeUsageTokenDetails(TypedDict):
     audio_tokens: ReadOnly[int]
     text_tokens: ReadOnly[int]
     cached_tokens: NotRequired[ReadOnly[int]]
+    cached_tokens_details: NotRequired[ReadOnly[OpenAIRealtimeCachedTokensDetails]]
 
 
 class OpenAIRealtimeResponseUsage(TypedDict):
diff --git a/litellm/types/utils.py b/litellm/types/utils.py
index 00c55b35182..1d73542c9bb 100644
--- a/litellm/types/utils.py
+++ b/litellm/types/utils.py
@@ -48,6 +48,7 @@ from litellm._logging import verbose_logger
 from litellm._uuid import uuid
 from litellm.types.llms.base import (
     BaseLiteLLMOpenAIResponseObject,
+    CachedTokensDetails,
     LiteLLMPydanticObjectBase,
 )
 from litellm.types.mcp import MCPServerCostInfo
@@ -252,6 +253,7 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
     cache_creation_input_token_cost_priority: float | None  # OpenAI priority service tier pricing
     cache_creation_input_token_cost_ultrafast: ReadOnly[float | None]  # OpenAI ultrafast service tier pricing
     cache_read_input_token_cost: float | None
+    cache_read_input_audio_token_cost: ReadOnly[float | None]
     cache_read_input_token_cost_flex: float | None  # OpenAI flex service tier pricing
     cache_read_input_token_cost_priority: float | None  # OpenAI priority service tier pricing
     cache_read_input_token_cost_ultrafast: ReadOnly[float | None]  # OpenAI ultrafast service tier pricing
@@ -1710,6 +1712,9 @@ class PromptTokensDetailsWrapper(
     cache_creation_token_details: CacheCreationTokenDetails | None = None
     """Details of cache creation tokens sent to the model. Used for tracking 5m/1h cache creation tokens for Anthropic prompt caching."""
 
+    cached_tokens_details: CachedTokensDetails | None = None
+    """Details of cached (cache-hit) tokens sent to the model. OpenAI realtime naming; carries the per-modality cache-read split."""
+
     def __setattr__(self, name: str, value: object) -> None:
         super().__setattr__(name, value)
         if name == "cache_write_tokens":
@@ -1756,6 +1761,8 @@ class PromptTokensDetailsWrapper(
             del self.cache_creation_tokens
         if self.cache_creation_token_details is None:
             del self.cache_creation_token_details
+        if self.cached_tokens_details is None:
+            del self.cached_tokens_details
 
 
 class ServerToolUse(BaseModel):
diff --git a/litellm/utils.py b/litellm/utils.py
index 7732cd88cb5..04139a124b6 100644
--- a/litellm/utils.py
+++ b/litellm/utils.py
@@ -5882,6 +5882,7 @@ def _get_model_info_helper(
                     "cache_creation_input_token_cost_ultrafast", None
                 ),
                 cache_read_input_token_cost=_model_info.get("cache_read_input_token_cost", None),
+                cache_read_input_audio_token_cost=_model_info.get("cache_read_input_audio_token_cost", None),
                 prompt_cache_min_tokens=_model_info.get("prompt_cache_min_tokens", None),
                 cache_read_input_token_cost_above_200k_tokens=_model_info.get(
                     "cache_read_input_token_cost_above_200k_tokens", None
diff --git a/model_prices_and_context_window.json b/model_prices_and_context_window.json
index 2220d0e1fe5..6d63fd8e3a9 100644
--- a/model_prices_and_context_window.json
+++ b/model_prices_and_context_window.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,
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 cbe6fe198c9..7cd19c65887 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
@@ -2648,6 +2648,7 @@ def test_cache_writing_cost_with_zero_creation_tokens_and_ephemeral_details():
 
     prompt_tokens_details: PromptTokensDetailsResult = {
         "cache_hit_tokens": 0,
+        "cache_hit_audio_tokens": 0,
         "cache_creation_tokens": 0,
         "cache_creation_token_details": CacheCreationTokenDetails(
             ephemeral_5m_input_tokens=100,
@@ -4005,6 +4006,7 @@ def test_billed_token_rates_follow_the_token_tier_the_breakdown_bills_at(monkeyp
         input_cost_per_token=6e-6,
         output_cost_per_token=3e-5,
         cache_read_input_token_cost=6e-7,
+        cache_read_input_audio_token_cost=6e-7,
         cache_creation_input_token_cost=7.5e-6,
         cache_creation_input_token_cost_above_1hr=0.0,
         output_cost_per_reasoning_token=3e-5,
@@ -5147,3 +5149,157 @@ def test_generic_cost_per_token_bills_nested_reasoning_once_beside_audio_output(
     assert completion_cost == pytest.approx(
         30 * info["output_cost_per_token"] + 70 * info["output_cost_per_audio_token"]
     )
+
+
+def test_cached_realtime_audio_tokens_billed_at_audio_cache_read_rate(
+    _local_model_cost_map: None,
+) -> None:
+    usage = Usage(
+        prompt_tokens=283,
+        completion_tokens=0,
+        total_tokens=283,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=116,
+            audio_tokens=167,
+            cached_tokens=192,
+            cached_tokens_details={"text_tokens": 64, "audio_tokens": 128},
+        ),
+    )
+
+    prompt_cost, _ = generic_cost_per_token(
+        model="gpt-realtime-2", usage=usage, custom_llm_provider="openai"
+    )
+    assert prompt_cost == pytest.approx(0.0015328)
+
+
+def test_prompt_tokens_details_without_cached_tokens_details_unchanged(
+    _local_model_cost_map: None,
+) -> None:
+    usage = Usage(
+        prompt_tokens=283,
+        completion_tokens=0,
+        total_tokens=283,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=116, audio_tokens=167, cached_tokens=192
+        ),
+    )
+
+    prompt_cost, _ = generic_cost_per_token(
+        model="gpt-realtime-2", usage=usage, custom_llm_provider="openai"
+    )
+    assert prompt_cost == pytest.approx(0.0029888)
+
+
+def test_cached_audio_tokens_fall_back_to_cache_read_input_token_cost() -> None:
+    model_info: ModelInfo = {
+        "input_cost_per_token": 4e-6,
+        "input_cost_per_audio_token": 32e-6,
+        "cache_read_input_token_cost": 5e-7,
+    }
+    usage = Usage(
+        prompt_tokens=283,
+        completion_tokens=0,
+        total_tokens=283,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=116,
+            audio_tokens=167,
+            cached_tokens=192,
+            cached_tokens_details={"text_tokens": 64, "audio_tokens": 128},
+        ),
+    )
+
+    prompt_cost, _ = generic_cost_per_token(
+        model="some-realtime-model",
+        usage=usage,
+        custom_llm_provider="openai",
+        model_info=model_info,
+    )
+    expected = 52 * 4e-6 + 64 * 5e-7 + 39 * 32e-6 + 128 * 5e-7
+    assert prompt_cost == pytest.approx(expected)
+
+
+def test_cached_audio_tokens_capped_at_cached_tokens(_local_model_cost_map: None) -> None:
+    """Nested cached_tokens_details exceeding cached_tokens must not over-subtract the audio bucket."""
+    usage = Usage(
+        prompt_tokens=283,
+        completion_tokens=0,
+        total_tokens=283,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=116,
+            audio_tokens=167,
+            cached_tokens=100,
+            cached_tokens_details={"audio_tokens": 128},
+        ),
+    )
+
+    prompt_cost, _ = generic_cost_per_token(
+        model="gpt-realtime-2", usage=usage, custom_llm_provider="openai"
+    )
+    assert prompt_cost == pytest.approx(116 * 4e-6 + (167 - 100) * 32e-6 + 100 * 4e-7)
+
+
+def test_cached_audio_tokens_billed_at_audio_cache_rate_through_model_info_lookup(_local_model_cost_map: None) -> None:
+    usage = Usage(
+        prompt_tokens=1000,
+        completion_tokens=0,
+        total_tokens=1000,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=400,
+            audio_tokens=600,
+            cached_tokens=500,
+            cached_tokens_details={"text_tokens": 100, "audio_tokens": 400},
+        ),
+    )
+
+    prompt_cost, _ = generic_cost_per_token(model="gpt-realtime-2.1-mini", usage=usage, custom_llm_provider="openai")
+    assert prompt_cost == pytest.approx(300 * 6e-7 + 100 * 6e-8 + 200 * 1e-5 + 400 * 3e-7)
+
+
+def test_cache_read_breakdown_splits_cached_audio_at_the_audio_cache_rate(_local_model_cost_map: None) -> None:
+    usage = Usage(
+        prompt_tokens=4863,
+        completion_tokens=1087,
+        total_tokens=5950,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=1693,
+            audio_tokens=3170,
+            cached_tokens=2816,
+            cached_tokens_details={"text_tokens": 896, "audio_tokens": 1920},
+        ),
+    )
+
+    breakdown = get_token_type_cost_breakdown(model="gpt-realtime-2.1-mini", custom_llm_provider="openai", usage=usage)
+    prompt_cost, _ = generic_cost_per_token(model="gpt-realtime-2.1-mini", usage=usage, custom_llm_provider="openai")
+
+    assert breakdown.cache_read_cost == pytest.approx(896 * 6e-8 + 1920 * 3e-7)
+    assert breakdown.rates is not None
+    assert breakdown.rates.cache_read_input_audio_token_cost == pytest.approx(3e-7)
+    assert prompt_cost == pytest.approx((1693 - 896) * 6e-7 + (3170 - 1920) * 1e-5 + breakdown.cache_read_cost)
+
+
+@pytest.mark.parametrize(
+    ("model", "custom_llm_provider", "expected_prompt_cost"),
+    (
+        pytest.param("azure/gpt-realtime-2025-08-28", "azure", 300 * 4e-6 + 100 * 4e-7 + 200 * 3.2e-5 + 400 * 4e-7, id="azure-gpt-realtime"),
+        pytest.param("azure/gpt-realtime-1.5-2026-02-23", "azure", 300 * 4e-6 + 100 * 4e-7 + 200 * 3.2e-5 + 400 * 4e-7, id="azure-gpt-realtime-1.5"),
+        pytest.param("azure/gpt-realtime-mini", "azure", 300 * 6e-7 + 100 * 6e-8 + 200 * 1e-5 + 400 * 3e-7, id="azure-gpt-realtime-mini"),
+        pytest.param("gpt-realtime-mini", "openai", 300 * 6e-7 + 100 * 6e-8 + 200 * 1e-5 + 400 * 3e-7, id="openai-gpt-realtime-mini"),
+    ),
+)
+def test_realtime_models_bill_cached_text_and_audio_at_their_cache_read_rates(
+    _local_model_cost_map: None, model: str, custom_llm_provider: str, expected_prompt_cost: float
+) -> None:
+    usage = Usage(
+        prompt_tokens=1000,
+        completion_tokens=0,
+        total_tokens=1000,
+        prompt_tokens_details=PromptTokensDetailsWrapper(
+            text_tokens=400,
+            audio_tokens=600,
+            cached_tokens=500,
+            cached_tokens_details={"text_tokens": 100, "audio_tokens": 400},
+        ),
+    )
+
+    prompt_cost, _ = generic_cost_per_token(model=model, usage=usage, custom_llm_provider=custom_llm_provider)
+    assert prompt_cost == pytest.approx(expected_prompt_cost)
diff --git a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py
index 3c78bbf79d7..d5a21bccad7 100644
--- a/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py
+++ b/tests/test_litellm/responses/litellm_completion_transformation/test_litellm_completion_responses.py
@@ -2789,6 +2789,7 @@ class TestUsageTransformation:
         assert response_usage.input_tokens_details is not None
         assert response_usage.input_tokens_details.cached_tokens == 5
         assert response_usage.input_tokens_details.text_tokens == 8
+        assert "cache_write_tokens" not in response_usage.input_tokens_details.model_dump()
 
     def test_transform_usage_with_cached_tokens_gemini(self):
         """Test that cached_tokens from Gemini are properly transformed to input_tokens_details"""
@@ -2851,6 +2852,7 @@ class TestUsageTransformation:
         assert response_usage.input_tokens_details is not None
         assert response_usage.input_tokens_details.cached_tokens == 100
         assert getattr(response_usage.input_tokens_details, "cache_write_tokens", None) == 800
+        assert response_usage.input_tokens_details.model_dump()["cache_write_tokens"] == 800
 
     def test_transform_usage_with_reasoning_tokens_gemini(self):
         """Test that reasoning_tokens from Gemini are properly transformed to output_tokens_details"""
diff --git a/tests/test_litellm/responses/test_responses_utils.py b/tests/test_litellm/responses/test_responses_utils.py
index 9d9eefdceb3..4d06b5e7bdc 100644
--- a/tests/test_litellm/responses/test_responses_utils.py
+++ b/tests/test_litellm/responses/test_responses_utils.py
@@ -577,6 +577,47 @@ class TestResponseAPILoggingUtils:
         assert result.completion_tokens_details is not None
         assert result.completion_tokens_details.reasoning_tokens == 4
 
+    def test_transform_realtime_usage_dict_keeps_cached_tokens_details(self):
+        usage = {
+            "input_tokens": 283,
+            "output_tokens": 0,
+            "total_tokens": 283,
+            "input_token_details": {
+                "text_tokens": 116,
+                "audio_tokens": 167,
+                "cached_tokens": 192,
+                "cached_tokens_details": {"text_tokens": 64, "audio_tokens": 128},
+            },
+        }
+
+        result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(usage)
+
+        assert result.prompt_tokens_details is not None
+        assert result.prompt_tokens_details.cached_tokens == 192
+        assert result.prompt_tokens_details.cached_tokens_details is not None
+        assert result.prompt_tokens_details.cached_tokens_details.audio_tokens == 128
+        assert result.prompt_tokens_details.cached_tokens_details.text_tokens == 64
+
+    def test_transform_response_api_usage_object_keeps_cached_tokens_details(self):
+        usage = ResponseAPIUsage(
+            input_tokens=283,
+            output_tokens=0,
+            total_tokens=283,
+            input_tokens_details={
+                "text_tokens": 116,
+                "audio_tokens": 167,
+                "cached_tokens": 192,
+                "cached_tokens_details": {"text_tokens": 64, "audio_tokens": 128},
+            },
+        )
+
+        result = ResponseAPILoggingUtils._transform_response_api_usage_to_chat_usage(usage)
+
+        assert result.prompt_tokens_details is not None
+        assert result.prompt_tokens_details.cached_tokens_details is not None
+        assert result.prompt_tokens_details.cached_tokens_details.audio_tokens == 128
+        assert result.prompt_tokens_details.cached_tokens_details.text_tokens == 64
+
 
 class TestResponsesAPIProviderSpecificParams:
     """
diff --git a/tests/test_litellm/test_cost_calculator.py b/tests/test_litellm/test_cost_calculator.py
index f2659cee3fd..68e9b6143a0 100644
--- a/tests/test_litellm/test_cost_calculator.py
+++ b/tests/test_litellm/test_cost_calculator.py
@@ -19,6 +19,7 @@ from litellm.cost_calculator import (
 )
 from litellm.litellm_core_utils.litellm_logging import Logging
 from litellm.llms.base_llm.ocr.transformation import OCRPage, OCRResponse, OCRUsageInfo
+from litellm.types.llms.base import CachedTokensDetails
 from litellm.types.llms.openai import OpenAIRealtimeStreamList
 from litellm.types.rerank import RerankResponse
 from litellm.types.utils import (
@@ -4848,6 +4849,109 @@ def test_collect_and_combine_realtime_usage_stores_partitioned_text_tokens() ->
     assert combined.completion_tokens_details.audio_tokens == 0
 
 
+def test_realtime_combine_sums_nested_cached_tokens_details():
+    results: OpenAIRealtimeStreamList = [
+        {
+            "type": "response.done",
+            "response": {
+                "usage": {
+                    "input_tokens": 283,
+                    "output_tokens": 0,
+                    "total_tokens": 283,
+                    "input_token_details": {
+                        "text_tokens": 116,
+                        "audio_tokens": 167,
+                        "cached_tokens": 192,
+                        "cached_tokens_details": {"text_tokens": 64, "audio_tokens": 128},
+                    },
+                }
+            },
+        },
+        {
+            "type": "response.done",
+            "response": {
+                "usage": {
+                    "input_tokens": 150,
+                    "output_tokens": 0,
+                    "total_tokens": 150,
+                    "input_token_details": {
+                        "text_tokens": 50,
+                        "audio_tokens": 100,
+                        "cached_tokens": 100,
+                        "cached_tokens_details": {"audio_tokens": 100},
+                    },
+                }
+            },
+        },
+    ]
+
+    combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
+        results=results,
+    )
+
+    assert combined.prompt_tokens_details is not None
+    assert combined.prompt_tokens_details.cached_tokens == 292
+    assert combined.prompt_tokens_details.cached_tokens_details is not None
+    assert combined.prompt_tokens_details.cached_tokens_details.audio_tokens == 228
+    assert combined.prompt_tokens_details.cached_tokens_details.text_tokens == 64
+    assert combined.prompt_tokens_details.cached_tokens_details.image_tokens is None
+
+
+@pytest.mark.parametrize("details_first", [True, False])
+def test_realtime_combine_keeps_cached_split_when_only_one_usage_has_details(details_first: bool):
+    with_details: Final = {
+        "type": "response.done",
+        "response": {
+            "usage": {
+                "input_tokens": 283,
+                "output_tokens": 0,
+                "total_tokens": 283,
+                "input_token_details": {
+                    "text_tokens": 116,
+                    "audio_tokens": 167,
+                    "cached_tokens": 192,
+                    "cached_tokens_details": {"text_tokens": 64, "audio_tokens": 128},
+                },
+            }
+        },
+    }
+    without_details: Final = {
+        "type": "response.done",
+        "response": {
+            "usage": {
+                "input_tokens": 150,
+                "output_tokens": 0,
+                "total_tokens": 150,
+                "input_token_details": {"text_tokens": 50, "audio_tokens": 100, "cached_tokens": 100},
+            }
+        },
+    }
+    results: OpenAIRealtimeStreamList = (
+        [with_details, without_details] if details_first else [without_details, with_details]
+    )
+
+    combined = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
+        results=results,
+    )
+
+    assert combined.prompt_tokens_details is not None
+    assert combined.prompt_tokens_details.cached_tokens == 292
+    assert combined.prompt_tokens_details.cached_tokens_details == CachedTokensDetails(text_tokens=64, audio_tokens=128)
+
+
+def test_usage_without_cached_tokens_details_omits_key():
+    usage = Usage(
+        prompt_tokens=10,
+        completion_tokens=5,
+        total_tokens=15,
+        prompt_tokens_details=PromptTokensDetailsWrapper(text_tokens=10),
+    )
+
+    dumped = usage.prompt_tokens_details.model_dump()
+    assert "cached_tokens_details" not in dumped
+    assert "cached_tokens_details" not in usage.prompt_tokens_details.model_dump_json()
+
+
 UNMAPPED_OCR_MODEL: Final = "azure_ai/some-unmapped-ocr-model-for-testing"
 MAPPED_OCR_MODEL: Final = "mistral/mistral-ocr-4-0"
 
diff --git a/tests/test_litellm/test_utils.py b/tests/test_litellm/test_utils.py
index d89496a0fd0..2ace005a8e2 100644
--- a/tests/test_litellm/test_utils.py
+++ b/tests/test_litellm/test_utils.py
@@ -6455,3 +6455,11 @@ def test_completion_finishes_response_metadata_before_handing_the_response_to_th
     assert snapshot["litellm_call_id"]
     assert snapshot["response_cost"] is not None
     assert snapshot["api_base"]
+
+
+def test_get_model_info_carries_cache_read_input_audio_token_cost(monkeypatch):
+    monkeypatch.setenv("LITELLM_LOCAL_MODEL_COST_MAP", "True")
+    monkeypatch.setattr(litellm, "model_cost", litellm.get_model_cost_map(url=""))
+    info = litellm.get_model_info("gpt-realtime-2.1-mini", custom_llm_provider="openai")
+    assert info["cache_read_input_audio_token_cost"] == 3e-07
+    assert info["cache_read_input_token_cost"] == 6e-08
diff --git a/ui/litellm-dashboard/src/lib/http/schema.d.ts b/ui/litellm-dashboard/src/lib/http/schema.d.ts
index 7eadaa6c991..839aa52fa84 100644
--- a/ui/litellm-dashboard/src/lib/http/schema.d.ts
+++ b/ui/litellm-dashboard/src/lib/http/schema.d.ts
@@ -16781,7 +16781,6 @@ export interface paths {
          *     - permissions: Optional[dict] - [Not Implemented Yet] User-specific permissions, eg. turning off pii masking.
          *     - metadata: Optional[dict] - Metadata for user, store information for user. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" }
          *     - max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x.
-         *     - soft_budget: Optional[float] - Get alerts when user crosses given budget, doesn't block requests.
          *     - model_max_budget: Optional[dict] - Model-specific max budget for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-budgets-to-keys)
          *     - budget_fallbacks: Optional[Dict[str, List[str]]] - Per-model fallback chain tried in order when that model's own `model_max_budget` is exceeded, e.g. {"gpt-4o": ["gpt-4o-mini"]}.
          *     - model_rpm_limit: Optional[float] - Model-specific rpm limit for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-limits-to-keys)
@@ -16887,7 +16886,6 @@ export interface paths {
          *         - permissions: Optional[dict] - [Not Implemented Yet] User-specific permissions, eg. turning off pii masking.
          *         - metadata: Optional[dict] - Metadata for user, store information for user. Example metadata = {"team": "core-infra", "app": "app2", "email": "ishaan@berri.ai" }
          *         - max_parallel_requests: Optional[int] - Rate limit a user based on the number of parallel requests. Raises 429 error, if user's parallel requests > x.
-         *         - soft_budget: Optional[float] - Get alerts when user crosses given budget, doesn't block requests.
          *         - model_max_budget: Optional[dict] - Model-specific max budget for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-budgets-to-keys)
          *         - budget_fallbacks: Optional[Dict[str, List[str]]] - Per-model fallback chain tried in order when that model's own `model_max_budget` is exceeded, e.g. {"gpt-4o": ["gpt-4o-mini"]}.
          *         - model_rpm_limit: Optional[float] - Model-specific rpm limit for user. [Docs](https://docs.litellm.ai/docs/proxy/users#add-model-specific-limits-to-keys)