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# What is this?
## Cost calculation for Google AI Studio / Vertex AI models
from typing import Literal, Optional, Tuple, Union
import litellm
from litellm import verbose_logger
from litellm.litellm_core_utils.llm_cost_calc.utils import _is_above_128k
"""
Gemini pricing covers:
- token
- image
- audio
- video
"""
"""
Vertex AI -> character based pricing
Google AI Studio -> token based pricing
"""
models_without_dynamic_pricing = ["gemini-1.0-pro", "gemini-pro"]
def cost_router(
model: str,
custom_llm_provider: str,
call_type: Union[Literal["embedding", "aembedding"], str],
) -> Literal["cost_per_character", "cost_per_token"]:
"""
Route the cost calc to the right place, based on model/call_type/etc.
Returns
- str, the specific google cost calc function it should route to.
"""
if custom_llm_provider == "vertex_ai" and (
"claude" in model
or "llama" in model
or "mistral" in model
or "jamba" in model
or "codestral" in model
):
return "cost_per_token"
elif custom_llm_provider == "vertex_ai" and (
call_type == "embedding" or call_type == "aembedding"
):
return "cost_per_token"
return "cost_per_character"
def cost_per_character(
model: str,
custom_llm_provider: str,
prompt_tokens: float,
completion_tokens: float,
prompt_characters: Optional[float] = None,
completion_characters: Optional[float] = None,
) -> Tuple[float, float]:
"""
Calculates the cost per character for a given VertexAI model, input messages, and response object.
Input:
- model: str, the model name without provider prefix
- custom_llm_provider: str, "vertex_ai-*"
- prompt_characters: float, the number of input characters
- completion_characters: float, the number of output characters
Returns:
Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
Raises:
Exception if model requires >128k pricing, but model cost not mapped
"""
model_info = litellm.get_model_info(
model=model, custom_llm_provider=custom_llm_provider
)
## GET MODEL INFO
model_info = litellm.get_model_info(
model=model, custom_llm_provider=custom_llm_provider
)
## CALCULATE INPUT COST
if prompt_characters is None:
prompt_cost, _ = cost_per_token(
model=model,
custom_llm_provider=custom_llm_provider,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
)
else:
try:
if (
_is_above_128k(tokens=prompt_characters * 4) # 1 token = 4 char
and model not in models_without_dynamic_pricing
):
## check if character pricing, else default to token pricing
assert (
"input_cost_per_character_above_128k_tokens" in model_info
and model_info["input_cost_per_character_above_128k_tokens"]
is not None
), "model info for model={} does not have 'input_cost_per_character_above_128k_tokens'-pricing for > 128k tokens\nmodel_info={}".format(
model, model_info
)
prompt_cost = (
prompt_characters
* model_info["input_cost_per_character_above_128k_tokens"]
)
else:
assert (
"input_cost_per_character" in model_info
and model_info["input_cost_per_character"] is not None
), "model info for model={} does not have 'input_cost_per_character'-pricing\nmodel_info={}".format(
model, model_info
)
prompt_cost = prompt_characters * model_info["input_cost_per_character"]
except Exception as e:
verbose_logger.debug(
"litellm.litellm_core_utils.llm_cost_calc.google.py::cost_per_character(): Exception occured - {}\nDefaulting to None".format(
str(e)
)
)
prompt_cost, _ = cost_per_token(
model=model,
custom_llm_provider=custom_llm_provider,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
)
## CALCULATE OUTPUT COST
if completion_characters is None:
_, completion_cost = cost_per_token(
model=model,
custom_llm_provider=custom_llm_provider,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
)
else:
try:
if (
_is_above_128k(tokens=completion_characters * 4) # 1 token = 4 char
and model not in models_without_dynamic_pricing
):
assert (
"output_cost_per_character_above_128k_tokens" in model_info
and model_info["output_cost_per_character_above_128k_tokens"]
is not None
), "model info for model={} does not have 'output_cost_per_character_above_128k_tokens' pricing\nmodel_info={}".format(
model, model_info
)
completion_cost = (
completion_tokens
* model_info["output_cost_per_character_above_128k_tokens"]
)
else:
assert (
"output_cost_per_character" in model_info
and model_info["output_cost_per_character"] is not None
), "model info for model={} does not have 'output_cost_per_character'-pricing\nmodel_info={}".format(
model, model_info
)
completion_cost = (
completion_characters * model_info["output_cost_per_character"]
)
except Exception as e:
verbose_logger.debug(
"litellm.litellm_core_utils.llm_cost_calc.google.py::cost_per_character(): Exception occured - {}\nDefaulting to None".format(
str(e)
)
)
_, completion_cost = cost_per_token(
model=model,
custom_llm_provider=custom_llm_provider,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
)
return prompt_cost, completion_cost
def cost_per_token(
model: str,
custom_llm_provider: str,
prompt_tokens: float,
completion_tokens: float,
) -> Tuple[float, float]:
"""
Calculates the cost per token for a given model, prompt tokens, and completion tokens.
Input:
- model: str, the model name without provider prefix
- custom_llm_provider: str, either "vertex_ai-*" or "gemini"
- prompt_tokens: float, the number of input tokens
- completion_tokens: float, the number of output tokens
Returns:
Tuple[float, float] - prompt_cost_in_usd, completion_cost_in_usd
Raises:
Exception if model requires >128k pricing, but model cost not mapped
"""
## GET MODEL INFO
model_info = litellm.get_model_info(
model=model, custom_llm_provider=custom_llm_provider
)
## CALCULATE INPUT COST
if (
_is_above_128k(tokens=prompt_tokens)
and model not in models_without_dynamic_pricing
):
assert (
"input_cost_per_token_above_128k_tokens" in model_info
and model_info["input_cost_per_token_above_128k_tokens"] is not None
), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format(
model, model_info
)
prompt_cost = (
prompt_tokens * model_info["input_cost_per_token_above_128k_tokens"]
)
else:
prompt_cost = prompt_tokens * model_info["input_cost_per_token"]
## CALCULATE OUTPUT COST
if (
_is_above_128k(tokens=completion_tokens)
and model not in models_without_dynamic_pricing
):
assert (
"output_cost_per_token_above_128k_tokens" in model_info
and model_info["output_cost_per_token_above_128k_tokens"] is not None
), "model info for model={} does not have pricing for > 128k tokens\nmodel_info={}".format(
model, model_info
)
completion_cost = (
completion_tokens * model_info["output_cost_per_token_above_128k_tokens"]
)
else:
completion_cost = completion_tokens * model_info["output_cost_per_token"]
return prompt_cost, completion_cost
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