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Add service_tier based pricing support for openai

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Sameerlite
2025-09-23 10:48:27 +05:30
parent 52a56bd5fe
commit d2208023d2
9 changed files with 378 additions and 11 deletions
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litellm/cost_calculator.py
+14 -1
View File
@@ -148,6 +148,8 @@ def cost_per_token( # noqa: PLR0915
### CALL TYPE ###
call_type: CallTypesLiteral = "completion",
audio_transcription_file_duration: float = 0.0, # for audio transcription calls - the file time in seconds
### SERVICE TIER ###
service_tier: Optional[str] = None, # for OpenAI service tier pricing
) -> Tuple[float, float]: # type: ignore
"""
Calculates the cost per token for a given model, prompt tokens, and completion tokens.
@@ -278,6 +280,7 @@ def cost_per_token( # noqa: PLR0915
model=model_without_prefix,
usage=usage_block,
custom_llm_provider=custom_llm_provider,
service_tier=service_tier,
)
return prompt_cost, completion_cost
@@ -327,7 +330,7 @@ def cost_per_token( # noqa: PLR0915
elif custom_llm_provider == "bedrock":
return bedrock_cost_per_token(model=model, usage=usage_block)
elif custom_llm_provider == "openai":
return openai_cost_per_token(model=model, usage=usage_block)
return openai_cost_per_token(model=model, usage=usage_block, service_tier=service_tier)
elif custom_llm_provider == "databricks":
return databricks_cost_per_token(model=model, usage=usage_block)
elif custom_llm_provider == "fireworks_ai":
@@ -606,6 +609,8 @@ def completion_cost( # noqa: PLR0915
litellm_model_name: Optional[str] = None,
router_model_id: Optional[str] = None,
litellm_logging_obj: Optional[LitellmLoggingObject] = None,
### SERVICE TIER ###
service_tier: Optional[str] = None, # for OpenAI service tier pricing
) -> float:
"""
Calculate the cost of a given completion call fot GPT-3.5-turbo, llama2, any litellm supported llm.
@@ -658,6 +663,10 @@ def completion_cost( # noqa: PLR0915
completion_response=completion_response
)
rerank_billed_units: Optional[RerankBilledUnits] = None
# Extract service_tier from optional_params if not provided directly
if service_tier is None and optional_params is not None:
service_tier = optional_params.get("service_tier")
selected_model = _select_model_name_for_cost_calc(
model=model,
@@ -909,6 +918,7 @@ def completion_cost( # noqa: PLR0915
call_type=cast(CallTypesLiteral, call_type),
audio_transcription_file_duration=audio_transcription_file_duration,
rerank_billed_units=rerank_billed_units,
service_tier=service_tier,
)
_final_cost = (
prompt_tokens_cost_usd_dollar + completion_tokens_cost_usd_dollar
@@ -1003,6 +1013,8 @@ def response_cost_calculator(
litellm_model_name: Optional[str] = None,
router_model_id: Optional[str] = None,
litellm_logging_obj: Optional[LitellmLoggingObject] = None,
### SERVICE TIER ###
service_tier: Optional[str] = None, # for OpenAI service tier pricing
) -> float:
"""
Returns
@@ -1036,6 +1048,7 @@ def response_cost_calculator(
litellm_model_name=litellm_model_name,
router_model_id=router_model_id,
litellm_logging_obj=litellm_logging_obj,
service_tier=service_tier,
)
return response_cost
except Exception as e:
litellm/litellm_core_utils/litellm_logging.py
+1
View File
@@ -1228,6 +1228,7 @@ class Logging(LiteLLMLoggingBaseClass):
"standard_built_in_tools_params": self.standard_built_in_tools_params,
"router_model_id": router_model_id,
"litellm_logging_obj": self,
"service_tier": self.optional_params.get("service_tier") if self.optional_params else None,
}
except Exception as e: # error creating kwargs for cost calculation
debug_info = StandardLoggingModelCostFailureDebugInformation(
litellm/litellm_core_utils/llm_cost_calc/utils.py
+60 -8
View File
@@ -4,7 +4,7 @@
from typing import Any, Literal, Optional, Tuple, TypedDict, cast
import litellm
from litellm._logging import verbose_logger
from litellm._logging import verbose_logger, verbose_proxy_logger
from litellm.types.utils import (
CacheCreationTokenDetails,
CallTypes,
@@ -12,6 +12,7 @@ from litellm.types.utils import (
ModelInfo,
PassthroughCallTypes,
Usage,
ServiceTier,
)
from litellm.utils import get_model_info
@@ -114,8 +115,30 @@ def _generic_cost_per_character(
return prompt_cost, completion_cost
def _get_service_tier_cost_key(base_key: str, service_tier: Optional[str]) -> str:
"""
Get the appropriate cost key based on service tier.
Args:
base_key: The base cost key (e.g., "input_cost_per_token")
service_tier: The service tier ("flex", "priority", or None for standard)
Returns:
str: The cost key to use (e.g., "input_cost_per_token_flex" or "input_cost_per_token")
"""
if service_tier is None:
return base_key
# Only use service tier specific keys for "flex" and "priority"
if service_tier.lower() in [ServiceTier.FLEX.value, ServiceTier.PRIORITY.value]:
return f"{base_key}_{service_tier.lower()}"
# For any other service tier, use standard pricing
return base_key
def _get_token_base_cost(
model_info: ModelInfo, usage: Usage
model_info: ModelInfo, usage: Usage, service_tier: Optional[str] = None
) -> Tuple[float, float, float, float, float]:
"""
Return prompt cost, completion cost, and cache costs for a given model and usage.
@@ -126,21 +149,27 @@ def _get_token_base_cost(
Returns:
Tuple[float, float, float, float] - (prompt_cost, completion_cost, cache_creation_cost, cache_read_cost)
"""
# Get service tier aware cost keys
input_cost_key = _get_service_tier_cost_key("input_cost_per_token", service_tier)
output_cost_key = _get_service_tier_cost_key("output_cost_per_token", service_tier)
cache_creation_cost_key = _get_service_tier_cost_key("cache_creation_input_token_cost", service_tier)
cache_read_cost_key = _get_service_tier_cost_key("cache_read_input_token_cost", service_tier)
prompt_base_cost = cast(
float, _get_cost_per_unit(model_info, "input_cost_per_token")
float, _get_cost_per_unit(model_info, input_cost_key)
)
completion_base_cost = cast(
float, _get_cost_per_unit(model_info, "output_cost_per_token")
float, _get_cost_per_unit(model_info, output_cost_key)
)
cache_creation_cost = cast(
float, _get_cost_per_unit(model_info, "cache_creation_input_token_cost")
float, _get_cost_per_unit(model_info, cache_creation_cost_key)
)
cache_creation_cost_above_1hr = cast(
float,
_get_cost_per_unit(model_info, "cache_creation_input_token_cost_above_1hr"),
)
cache_read_cost = cast(
float, _get_cost_per_unit(model_info, "cache_read_input_token_cost")
float, _get_cost_per_unit(model_info, cache_read_cost_key)
)
## CHECK IF ABOVE THRESHOLD
@@ -249,6 +278,29 @@ def _get_cost_per_unit(
verbose_logger.exception(
f"litellm.litellm_core_utils.llm_cost_calc.utils.py::calculate_cost_per_component(): Exception occured - {cost_per_unit}\nDefaulting to 0.0"
)
# If the service tier key doesn't exist or is None, try to fall back to the standard key
if cost_per_unit is None:
# Check if any service tier suffix exists in the cost key using ServiceTier enum
for service_tier in ServiceTier:
suffix = f"_{service_tier.value}"
if suffix in cost_key:
# Extract the base key by removing the matched suffix
base_key = cost_key.replace(suffix, '')
fallback_cost = model_info.get(base_key)
if isinstance(fallback_cost, float):
return fallback_cost
if isinstance(fallback_cost, int):
return float(fallback_cost)
if isinstance(fallback_cost, str):
try:
return float(fallback_cost)
except ValueError:
verbose_logger.exception(
f"litellm.litellm_core_utils.llm_cost_calc.utils.py::_get_cost_per_unit(): Exception occured - {fallback_cost}\nDefaulting to 0.0"
)
break # Only try the first matching suffix
return default_value
@@ -443,7 +495,7 @@ def _calculate_input_cost(
def generic_cost_per_token(
model: str, usage: Usage, custom_llm_provider: str
model: str, usage: Usage, custom_llm_provider: str, service_tier: Optional[str] = None
) -> Tuple[float, float]:
"""
Calculates the cost per token for a given model, prompt tokens, and completion tokens.
@@ -495,7 +547,7 @@ def generic_cost_per_token(
cache_creation_cost,
cache_creation_cost_above_1hr,
cache_read_cost,
) = _get_token_base_cost(model_info=model_info, usage=usage)
) = _get_token_base_cost(model_info=model_info, usage=usage, service_tier=service_tier)
prompt_cost = _calculate_input_cost(
prompt_tokens_details=prompt_tokens_details,
litellm/llms/openai/cost_calculation.py
+2 -2
View File
@@ -18,7 +18,7 @@ def cost_router(call_type: CallTypes) -> Literal["cost_per_token", "cost_per_sec
return "cost_per_token"
def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
def cost_per_token(model: str, usage: Usage, service_tier: Optional[str] = None) -> Tuple[float, float]:
"""
Calculates the cost per token for a given model, prompt tokens, and completion tokens.
@@ -31,7 +31,7 @@ def cost_per_token(model: str, usage: Usage) -> Tuple[float, float]:
"""
## CALCULATE INPUT COST
return generic_cost_per_token(
model=model, usage=usage, custom_llm_provider="openai"
model=model, usage=usage, custom_llm_provider="openai", service_tier=service_tier
)
# ### Non-cached text tokens
# non_cached_text_tokens = usage.prompt_tokens
litellm/model_prices_and_context_window_backup.json
+59
View File
@@ -11534,8 +11534,10 @@
},
"gpt-4.1": {
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_priority": 8.75e-07,
"input_cost_per_token": 2e-06,
"input_cost_per_token_batches": 1e-06,
"input_cost_per_token_priority": 3.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
@@ -11543,6 +11545,7 @@
"mode": "chat",
"output_cost_per_token": 8e-06,
"output_cost_per_token_batches": 4e-06,
"output_cost_per_token_priority": 1.4e-05,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -11600,8 +11603,10 @@
},
"gpt-4.1-mini": {
"cache_read_input_token_cost": 1e-07,
"cache_read_input_token_cost_priority": 1.75e-07,
"input_cost_per_token": 4e-07,
"input_cost_per_token_batches": 2e-07,
"input_cost_per_token_priority": 7e-07,
"litellm_provider": "openai",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
@@ -11609,6 +11614,7 @@
"mode": "chat",
"output_cost_per_token": 1.6e-06,
"output_cost_per_token_batches": 8e-07,
"output_cost_per_token_priority": 2.8e-06,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -11666,8 +11672,10 @@
},
"gpt-4.1-nano": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_priority": 5e-08,
"input_cost_per_token": 1e-07,
"input_cost_per_token_batches": 5e-08,
"input_cost_per_token_priority": 2e-07,
"litellm_provider": "openai",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
@@ -11675,6 +11683,7 @@
"mode": "chat",
"output_cost_per_token": 4e-07,
"output_cost_per_token_batches": 2e-07,
"output_cost_per_token_priority": 8e-07,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -11773,8 +11782,10 @@
},
"gpt-4o": {
"cache_read_input_token_cost": 1.25e-06,
"cache_read_input_token_cost_priority": 2.125e-06,
"input_cost_per_token": 2.5e-06,
"input_cost_per_token_batches": 1.25e-06,
"input_cost_per_token_priority": 4.25e-06,
"litellm_provider": "openai",
"max_input_tokens": 128000,
"max_output_tokens": 16384,
@@ -11782,6 +11793,7 @@
"mode": "chat",
"output_cost_per_token": 1e-05,
"output_cost_per_token_batches": 5e-06,
"output_cost_per_token_priority": 1.7e-05,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
@@ -11794,6 +11806,7 @@
"gpt-4o-2024-05-13": {
"input_cost_per_token": 5e-06,
"input_cost_per_token_batches": 2.5e-06,
"input_cost_per_token_priority": 8.75e-06,
"litellm_provider": "openai",
"max_input_tokens": 128000,
"max_output_tokens": 4096,
@@ -11801,6 +11814,7 @@
"mode": "chat",
"output_cost_per_token": 1.5e-05,
"output_cost_per_token_batches": 7.5e-06,
"output_cost_per_token_priority": 2.625e-05,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
@@ -11919,8 +11933,10 @@
},
"gpt-4o-mini": {
"cache_read_input_token_cost": 7.5e-08,
"cache_read_input_token_cost_priority": 1.25e-07,
"input_cost_per_token": 1.5e-07,
"input_cost_per_token_batches": 7.5e-08,
"input_cost_per_token_priority": 2.5e-07,
"litellm_provider": "openai",
"max_input_tokens": 128000,
"max_output_tokens": 16384,
@@ -11928,6 +11944,7 @@
"mode": "chat",
"output_cost_per_token": 6e-07,
"output_cost_per_token_batches": 3e-07,
"output_cost_per_token_priority": 1e-06,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
@@ -12243,13 +12260,19 @@
},
"gpt-5": {
"cache_read_input_token_cost": 1.25e-07,
"cache_read_input_token_cost_flex": 6.25e-08,
"cache_read_input_token_cost_priority": 2.5e-07,
"input_cost_per_token": 1.25e-06,
"input_cost_per_token_flex": 6.25e-07,
"input_cost_per_token_priority": 2.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1e-05,
"output_cost_per_token_flex": 5e-06,
"output_cost_per_token_priority": 2e-05,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12275,13 +12298,19 @@
},
"gpt-5-2025-08-07": {
"cache_read_input_token_cost": 1.25e-07,
"cache_read_input_token_cost_flex": 6.25e-08,
"cache_read_input_token_cost_priority": 2.5e-07,
"input_cost_per_token": 1.25e-06,
"input_cost_per_token_flex": 6.25e-07,
"input_cost_per_token_priority": 2.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1e-05,
"output_cost_per_token_flex": 5e-06,
"output_cost_per_token_priority": 2e-05,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12371,13 +12400,19 @@
},
"gpt-5-mini": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_flex": 1.25e-08,
"cache_read_input_token_cost_priority": 4.5e-08,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_flex": 1.25e-07,
"input_cost_per_token_priority": 4.5e-07,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 2e-06,
"output_cost_per_token_flex": 1e-06,
"output_cost_per_token_priority": 3.6e-06,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12403,13 +12438,19 @@
},
"gpt-5-mini-2025-08-07": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_flex": 1.25e-08,
"cache_read_input_token_cost_priority": 4.5e-08,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_flex": 1.25e-07,
"input_cost_per_token_priority": 4.5e-07,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 2e-06,
"output_cost_per_token_flex": 1e-06,
"output_cost_per_token_priority": 3.6e-06,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12435,13 +12476,16 @@
},
"gpt-5-nano": {
"cache_read_input_token_cost": 5e-09,
"cache_read_input_token_cost_flex": 2.5e-09,
"input_cost_per_token": 5e-08,
"input_cost_per_token_flex": 2.5e-08,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 4e-07,
"output_cost_per_token_flex": 2e-07,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12467,13 +12511,16 @@
},
"gpt-5-nano-2025-08-07": {
"cache_read_input_token_cost": 5e-09,
"cache_read_input_token_cost_flex": 2.5e-09,
"input_cost_per_token": 5e-08,
"input_cost_per_token_flex": 2.5e-08,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 4e-07,
"output_cost_per_token_flex": 2e-07,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -15177,13 +15224,19 @@
},
"o3": {
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_flex": 2.5e-07,
"cache_read_input_token_cost_priority": 8.75e-07,
"input_cost_per_token": 2e-06,
"input_cost_per_token_flex": 1e-06,
"input_cost_per_token_priority": 3.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"output_cost_per_token": 8e-06,
"output_cost_per_token_flex": 4e-06,
"output_cost_per_token_priority": 1.4e-05,
"supported_endpoints": [
"/v1/responses",
"/v1/chat/completions",
@@ -15399,13 +15452,19 @@
},
"o4-mini": {
"cache_read_input_token_cost": 2.75e-07,
"cache_read_input_token_cost_flex": 1.38e-07,
"cache_read_input_token_cost_priority": 5e-07,
"input_cost_per_token": 1.1e-06,
"input_cost_per_token_flex": 5.5e-07,
"input_cost_per_token_priority": 2e-06,
"litellm_provider": "openai",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"output_cost_per_token": 4.4e-06,
"output_cost_per_token_flex": 2.2e-06,
"output_cost_per_token_priority": 8e-06,
"supports_function_calling": true,
"supports_parallel_function_calling": false,
"supports_pdf_input": true,
litellm/types/utils.py
+12
View File
@@ -122,9 +122,13 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
max_input_tokens: Required[Optional[int]]
max_output_tokens: Required[Optional[int]]
input_cost_per_token: Required[float]
input_cost_per_token_flex: Optional[float] # OpenAI flex service tier pricing
input_cost_per_token_priority: Optional[float] # OpenAI priority service tier pricing
cache_creation_input_token_cost: Optional[float]
cache_creation_input_token_cost_above_1hr: Optional[float]
cache_read_input_token_cost: Optional[float]
cache_read_input_token_cost_flex: Optional[float] # OpenAI flex service tier pricing
cache_read_input_token_cost_priority: Optional[float] # OpenAI priority service tier pricing
input_cost_per_character: Optional[float] # only for vertex ai models
input_cost_per_audio_token: Optional[float]
input_cost_per_token_above_128k_tokens: Optional[float] # only for vertex ai models
@@ -142,6 +146,8 @@ class ModelInfoBase(ProviderSpecificModelInfo, total=False):
input_cost_per_token_batches: Optional[float]
output_cost_per_token_batches: Optional[float]
output_cost_per_token: Required[float]
output_cost_per_token_flex: Optional[float] # OpenAI flex service tier pricing
output_cost_per_token_priority: Optional[float] # OpenAI priority service tier pricing
output_cost_per_character: Optional[float] # only for vertex ai models
output_cost_per_audio_token: Optional[float]
output_cost_per_token_above_128k_tokens: Optional[
@@ -2583,6 +2589,12 @@ class SpecialEnums(Enum):
LITELLM_MANAGED_GENERIC_RESPONSE_COMPLETE_STR = "litellm_proxy;model_id:{};generic_response_id:{}" # generic implementation of 'managed batches' - used for finetuning and any future work.
class ServiceTier(Enum):
"""Enum for service tier types used in cost calculations."""
FLEX = "flex"
PRIORITY = "priority"
LLMResponseTypes = Union[
ModelResponse,
EmbeddingResponse,
litellm/utils.py
+6
View File
@@ -4874,12 +4874,16 @@ def _get_model_info_helper( # noqa: PLR0915
max_input_tokens=_model_info.get("max_input_tokens", None),
max_output_tokens=_model_info.get("max_output_tokens", None),
input_cost_per_token=_input_cost_per_token,
input_cost_per_token_flex=_model_info.get("input_cost_per_token_flex", None),
input_cost_per_token_priority=_model_info.get("input_cost_per_token_priority", None),
cache_creation_input_token_cost=_model_info.get(
"cache_creation_input_token_cost", None
),
cache_read_input_token_cost=_model_info.get(
"cache_read_input_token_cost", None
),
cache_read_input_token_cost_flex=_model_info.get("cache_read_input_token_cost_flex", None),
cache_read_input_token_cost_priority=_model_info.get("cache_read_input_token_cost_priority", None),
cache_creation_input_token_cost_above_1hr=_model_info.get(
"cache_creation_input_token_cost_above_1hr", None
),
@@ -4904,6 +4908,8 @@ def _get_model_info_helper( # noqa: PLR0915
"output_cost_per_token_batches"
),
output_cost_per_token=_output_cost_per_token,
output_cost_per_token_flex=_model_info.get("output_cost_per_token_flex", None),
output_cost_per_token_priority=_model_info.get("output_cost_per_token_priority", None),
output_cost_per_audio_token=_model_info.get(
"output_cost_per_audio_token", None
),
model_prices_and_context_window.json
+61
View File
@@ -11534,8 +11534,10 @@
},
"gpt-4.1": {
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_priority": 8.75e-07,
"input_cost_per_token": 2e-06,
"input_cost_per_token_batches": 1e-06,
"input_cost_per_token_priority": 3.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
@@ -11543,6 +11545,7 @@
"mode": "chat",
"output_cost_per_token": 8e-06,
"output_cost_per_token_batches": 4e-06,
"output_cost_per_token_priority": 1.4e-05,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -11600,8 +11603,10 @@
},
"gpt-4.1-mini": {
"cache_read_input_token_cost": 1e-07,
"cache_read_input_token_cost_priority": 1.75e-07,
"input_cost_per_token": 4e-07,
"input_cost_per_token_batches": 2e-07,
"input_cost_per_token_priority": 7e-07,
"litellm_provider": "openai",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
@@ -11609,6 +11614,7 @@
"mode": "chat",
"output_cost_per_token": 1.6e-06,
"output_cost_per_token_batches": 8e-07,
"output_cost_per_token_priority": 2.8e-06,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -11666,8 +11672,10 @@
},
"gpt-4.1-nano": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_priority": 5e-08,
"input_cost_per_token": 1e-07,
"input_cost_per_token_batches": 5e-08,
"input_cost_per_token_priority": 2e-07,
"litellm_provider": "openai",
"max_input_tokens": 1047576,
"max_output_tokens": 32768,
@@ -11675,6 +11683,7 @@
"mode": "chat",
"output_cost_per_token": 4e-07,
"output_cost_per_token_batches": 2e-07,
"output_cost_per_token_priority": 8e-07,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -11773,8 +11782,10 @@
},
"gpt-4o": {
"cache_read_input_token_cost": 1.25e-06,
"cache_read_input_token_cost_priority": 2.125e-06,
"input_cost_per_token": 2.5e-06,
"input_cost_per_token_batches": 1.25e-06,
"input_cost_per_token_priority": 4.25e-06,
"litellm_provider": "openai",
"max_input_tokens": 128000,
"max_output_tokens": 16384,
@@ -11782,6 +11793,7 @@
"mode": "chat",
"output_cost_per_token": 1e-05,
"output_cost_per_token_batches": 5e-06,
"output_cost_per_token_priority": 1.7e-05,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
@@ -11794,6 +11806,7 @@
"gpt-4o-2024-05-13": {
"input_cost_per_token": 5e-06,
"input_cost_per_token_batches": 2.5e-06,
"input_cost_per_token_priority": 8.75e-06,
"litellm_provider": "openai",
"max_input_tokens": 128000,
"max_output_tokens": 4096,
@@ -11801,6 +11814,7 @@
"mode": "chat",
"output_cost_per_token": 1.5e-05,
"output_cost_per_token_batches": 7.5e-06,
"output_cost_per_token_priority": 2.625e-05,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
@@ -11919,8 +11933,10 @@
},
"gpt-4o-mini": {
"cache_read_input_token_cost": 7.5e-08,
"cache_read_input_token_cost_priority": 1.25e-07,
"input_cost_per_token": 1.5e-07,
"input_cost_per_token_batches": 7.5e-08,
"input_cost_per_token_priority": 2.5e-07,
"litellm_provider": "openai",
"max_input_tokens": 128000,
"max_output_tokens": 16384,
@@ -11928,6 +11944,7 @@
"mode": "chat",
"output_cost_per_token": 6e-07,
"output_cost_per_token_batches": 3e-07,
"output_cost_per_token_priority": 1e-06,
"supports_function_calling": true,
"supports_parallel_function_calling": true,
"supports_pdf_input": true,
@@ -12243,13 +12260,19 @@
},
"gpt-5": {
"cache_read_input_token_cost": 1.25e-07,
"cache_read_input_token_cost_flex": 6.25e-08,
"cache_read_input_token_cost_priority": 2.5e-07,
"input_cost_per_token": 1.25e-06,
"input_cost_per_token_flex": 6.25e-07,
"input_cost_per_token_priority": 2.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1e-05,
"output_cost_per_token_flex": 5e-06,
"output_cost_per_token_priority": 2e-05,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12275,13 +12298,19 @@
},
"gpt-5-2025-08-07": {
"cache_read_input_token_cost": 1.25e-07,
"cache_read_input_token_cost_flex": 6.25e-08,
"cache_read_input_token_cost_priority": 2.5e-07,
"input_cost_per_token": 1.25e-06,
"input_cost_per_token_flex": 6.25e-07,
"input_cost_per_token_priority": 2.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1e-05,
"output_cost_per_token_flex": 5e-06,
"output_cost_per_token_priority": 2e-05,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12303,6 +12332,7 @@
"supports_response_schema": true,
"supports_system_messages": true,
"supports_tool_choice": true,
"supports_service_tier": true,
"supports_vision": true
},
"gpt-5-chat": {
@@ -12371,13 +12401,19 @@
},
"gpt-5-mini": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_flex": 1.25e-08,
"cache_read_input_token_cost_priority": 4.5e-08,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_flex": 1.25e-07,
"input_cost_per_token_priority": 4.5e-07,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 2e-06,
"output_cost_per_token_flex": 1e-06,
"output_cost_per_token_priority": 3.6e-06,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12403,13 +12439,19 @@
},
"gpt-5-mini-2025-08-07": {
"cache_read_input_token_cost": 2.5e-08,
"cache_read_input_token_cost_flex": 1.25e-08,
"cache_read_input_token_cost_priority": 4.5e-08,
"input_cost_per_token": 2.5e-07,
"input_cost_per_token_flex": 1.25e-07,
"input_cost_per_token_priority": 4.5e-07,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 2e-06,
"output_cost_per_token_flex": 1e-06,
"output_cost_per_token_priority": 3.6e-06,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12435,13 +12477,17 @@
},
"gpt-5-nano": {
"cache_read_input_token_cost": 5e-09,
"cache_read_input_token_cost_flex": 2.5e-09,
"input_cost_per_token": 5e-08,
"input_cost_per_token_flex": 2.5e-08,
"input_cost_per_token_priority": 2.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 4e-07,
"output_cost_per_token_flex": 2e-07,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -12467,13 +12513,16 @@
},
"gpt-5-nano-2025-08-07": {
"cache_read_input_token_cost": 5e-09,
"cache_read_input_token_cost_flex": 2.5e-09,
"input_cost_per_token": 5e-08,
"input_cost_per_token_flex": 2.5e-08,
"litellm_provider": "openai",
"max_input_tokens": 400000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 4e-07,
"output_cost_per_token_flex": 2e-07,
"supported_endpoints": [
"/v1/chat/completions",
"/v1/batch",
@@ -15177,13 +15226,19 @@
},
"o3": {
"cache_read_input_token_cost": 5e-07,
"cache_read_input_token_cost_flex": 2.5e-07,
"cache_read_input_token_cost_priority": 8.75e-07,
"input_cost_per_token": 2e-06,
"input_cost_per_token_flex": 1e-06,
"input_cost_per_token_priority": 3.5e-06,
"litellm_provider": "openai",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"output_cost_per_token": 8e-06,
"output_cost_per_token_flex": 4e-06,
"output_cost_per_token_priority": 1.4e-05,
"supported_endpoints": [
"/v1/responses",
"/v1/chat/completions",
@@ -15399,13 +15454,19 @@
},
"o4-mini": {
"cache_read_input_token_cost": 2.75e-07,
"cache_read_input_token_cost_flex": 1.375e-07,
"cache_read_input_token_cost_priority": 5e-07,
"input_cost_per_token": 1.1e-06,
"input_cost_per_token_flex": 5.5e-07,
"input_cost_per_token_priority": 2e-06,
"litellm_provider": "openai",
"max_input_tokens": 200000,
"max_output_tokens": 100000,
"max_tokens": 100000,
"mode": "chat",
"output_cost_per_token": 4.4e-06,
"output_cost_per_token_flex": 2.2e-06,
"output_cost_per_token_priority": 8e-06,
"supports_function_calling": true,
"supports_parallel_function_calling": false,
"supports_pdf_input": true,
tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
+163
View File
@@ -463,3 +463,166 @@ def test_calculate_cache_writing_cost():
)
assert result_zero == 0.0
def test_service_tier_flex_pricing():
"""Test that flex service tier uses correct pricing (approximately 50% of standard)."""
# Set up environment for local model cost map
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
# Test with gpt-5-nano which has flex pricing
model = "gpt-5-nano"
custom_llm_provider = "openai"
# Create usage object
usage = Usage(
prompt_tokens=1000,
completion_tokens=500,
total_tokens=1500
)
# Test standard pricing
std_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
service_tier=None
)
std_total = std_cost[0] + std_cost[1]
# Test flex pricing
flex_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
service_tier="flex"
)
flex_total = flex_cost[0] + flex_cost[1]
# Verify flex is approximately 50% of standard
assert std_total > 0, "Standard cost should be greater than 0"
assert flex_total > 0, "Flex cost should be greater than 0"
flex_ratio = flex_total / std_total
assert 0.45 <= flex_ratio <= 0.55, f"Flex pricing should be ~50% of standard, got {flex_ratio:.2f}"
# Verify specific costs match expected values
# gpt-5-nano flex: input=2.5e-08, output=2e-07
expected_flex_prompt = 1000 * 2.5e-08 # 0.000025
expected_flex_completion = 500 * 2e-07 # 0.0001
expected_flex_total = expected_flex_prompt + expected_flex_completion
assert abs(flex_cost[0] - expected_flex_prompt) < 1e-10, f"Flex prompt cost mismatch: {flex_cost[0]} vs {expected_flex_prompt}"
assert abs(flex_cost[1] - expected_flex_completion) < 1e-10, f"Flex completion cost mismatch: {flex_cost[1]} vs {expected_flex_completion}"
assert abs(flex_total - expected_flex_total) < 1e-10, f"Flex total cost mismatch: {flex_total} vs {expected_flex_total}"
def test_service_tier_default_pricing():
"""Test that when no service tier is provided, standard pricing is used."""
# Set up environment for local model cost map
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
# Test with gpt-5-nano
model = "gpt-5-nano"
custom_llm_provider = "openai"
# Create usage object
usage = Usage(
prompt_tokens=1000,
completion_tokens=500,
total_tokens=1500
)
# Test with no service tier (should use standard)
default_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
service_tier=None
)
# Test with explicit standard service tier
standard_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
service_tier="standard"
)
# Both should be identical
assert abs(default_cost[0] - standard_cost[0]) < 1e-10, "Default and standard prompt costs should be identical"
assert abs(default_cost[1] - standard_cost[1]) < 1e-10, "Default and standard completion costs should be identical"
# Verify specific costs match expected standard values
# gpt-5-nano standard: input=5e-08, output=4e-07
expected_standard_prompt = 1000 * 5e-08 # 0.00005
expected_standard_completion = 500 * 4e-07 # 0.0002
expected_standard_total = expected_standard_prompt + expected_standard_completion
assert abs(default_cost[0] - expected_standard_prompt) < 1e-10, f"Standard prompt cost mismatch: {default_cost[0]} vs {expected_standard_prompt}"
assert abs(default_cost[1] - expected_standard_completion) < 1e-10, f"Standard completion cost mismatch: {default_cost[1]} vs {expected_standard_completion}"
def test_service_tier_fallback_pricing():
"""Test that when service tier is provided but model doesn't have those keys, it falls back to standard pricing."""
# Set up environment for local model cost map
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
# Test with gpt-4 which doesn't have flex pricing keys
model = "gpt-4"
custom_llm_provider = "openai"
# Create usage object
usage = Usage(
prompt_tokens=1000,
completion_tokens=500,
total_tokens=1500
)
# Test standard pricing
std_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
service_tier=None
)
std_total = std_cost[0] + std_cost[1]
# Test flex pricing (should fall back to standard since gpt-4 doesn't have flex keys)
flex_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
service_tier="flex"
)
flex_total = flex_cost[0] + flex_cost[1]
# Test priority pricing (should fall back to standard since gpt-4 doesn't have priority keys)
priority_cost = generic_cost_per_token(
model=model,
usage=usage,
custom_llm_provider=custom_llm_provider,
service_tier="priority"
)
priority_total = priority_cost[0] + priority_cost[1]
# All should be identical (fallback to standard)
assert abs(std_total - flex_total) < 1e-10, f"Standard and flex costs should be identical (fallback): {std_total} vs {flex_total}"
assert abs(std_total - priority_total) < 1e-10, f"Standard and priority costs should be identical (fallback): {std_total} vs {priority_total}"
# Verify costs are reasonable (not zero)
assert std_total > 0, "Standard cost should be greater than 0"
assert flex_total > 0, "Flex cost should be greater than 0 (fallback)"
assert priority_total > 0, "Priority cost should be greater than 0 (fallback)"
# Verify specific costs match expected gpt-4 values
# gpt-4 standard: input=3e-05, output=6e-05
expected_standard_prompt = 1000 * 3e-05 # 0.03
expected_standard_completion = 500 * 6e-05 # 0.03
expected_standard_total = expected_standard_prompt + expected_standard_completion
assert abs(std_cost[0] - expected_standard_prompt) < 1e-10, f"Standard prompt cost mismatch: {std_cost[0]} vs {expected_standard_prompt}"
assert abs(std_cost[1] - expected_standard_completion) < 1e-10, f"Standard completion cost mismatch: {std_cost[1]} vs {expected_standard_completion}"