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import json
import os
import sys
import pytest
sys.path.insert(
0, os.path.abspath("../..")
) # Adds the parent directory to the system path
from unittest.mock import MagicMock, patch
from pydantic import BaseModel
import litellm
from litellm.cost_calculator import (
handle_realtime_stream_cost_calculation,
response_cost_calculator,
)
from litellm.types.llms.openai import OpenAIRealtimeStreamList
from litellm.types.utils import ModelResponse, PromptTokensDetailsWrapper, Usage
def test_cost_calculator_with_response_cost_in_additional_headers():
class MockResponse(BaseModel):
_hidden_params = {
"additional_headers": {"llm_provider-x-litellm-response-cost": 1000}
}
result = response_cost_calculator(
response_object=MockResponse(),
model="",
custom_llm_provider=None,
call_type="",
optional_params={},
cache_hit=None,
base_model=None,
)
assert result == 1000
def test_cost_calculator_with_usage():
from litellm import get_model_info
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
usage = Usage(
prompt_tokens=100,
completion_tokens=100,
prompt_tokens_details=PromptTokensDetailsWrapper(
text_tokens=10, audio_tokens=90
),
)
mr = ModelResponse(usage=usage, model="gemini-2.0-flash-001")
result = response_cost_calculator(
response_object=mr,
model="",
custom_llm_provider="vertex_ai",
call_type="acompletion",
optional_params={},
cache_hit=None,
base_model=None,
)
model_info = litellm.model_cost["gemini-2.0-flash-001"]
expected_cost = (
usage.prompt_tokens_details.audio_tokens
* model_info["input_cost_per_audio_token"]
+ usage.prompt_tokens_details.text_tokens * model_info["input_cost_per_token"]
+ usage.completion_tokens * model_info["output_cost_per_token"]
)
assert result == expected_cost, f"Got {result}, Expected {expected_cost}"
def test_handle_realtime_stream_cost_calculation():
from litellm.cost_calculator import RealtimeAPITokenUsageProcessor
# Setup test data
results: OpenAIRealtimeStreamList = [
{"type": "session.created", "session": {"model": "gpt-3.5-turbo"}},
{
"type": "response.done",
"response": {
"usage": {"input_tokens": 100, "output_tokens": 50, "total_tokens": 150}
},
},
{
"type": "response.done",
"response": {
"usage": {
"input_tokens": 200,
"output_tokens": 100,
"total_tokens": 300,
}
},
},
]
combined_usage_object = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
results=results,
)
# Test with explicit model name
cost = handle_realtime_stream_cost_calculation(
results=results,
combined_usage_object=combined_usage_object,
custom_llm_provider="openai",
litellm_model_name="gpt-3.5-turbo",
)
# Calculate expected cost
# gpt-3.5-turbo costs: $0.0015/1K tokens input, $0.002/1K tokens output
expected_cost = (300 * 0.0015 / 1000) + ( # input tokens (100 + 200)
150 * 0.002 / 1000
) # output tokens (50 + 100)
assert (
abs(cost - expected_cost) <= 0.00075
) # Allow small floating point differences
# Test with different model name in session
results[0]["session"]["model"] = "gpt-4"
cost = handle_realtime_stream_cost_calculation(
results=results,
combined_usage_object=combined_usage_object,
custom_llm_provider="openai",
litellm_model_name="gpt-3.5-turbo",
)
# Calculate expected cost using gpt-4 rates
# gpt-4 costs: $0.03/1K tokens input, $0.06/1K tokens output
expected_cost = (300 * 0.03 / 1000) + ( # input tokens
150 * 0.06 / 1000
) # output tokens
assert abs(cost - expected_cost) < 0.00076
# Test with no response.done events
results = [{"type": "session.created", "session": {"model": "gpt-3.5-turbo"}}]
combined_usage_object = RealtimeAPITokenUsageProcessor.collect_and_combine_usage_from_realtime_stream_results(
results=results,
)
cost = handle_realtime_stream_cost_calculation(
results=results,
combined_usage_object=combined_usage_object,
custom_llm_provider="openai",
litellm_model_name="gpt-3.5-turbo",
)
assert cost == 0.0 # No usage, no cost
def test_custom_pricing_with_router_model_id():
from litellm import Router
router = Router(
model_list=[
{
"model_name": "prod/claude-3-5-sonnet-20240620",
"litellm_params": {
"model": "anthropic/claude-3-5-sonnet-20240620",
"api_key": "test_api_key",
},
"model_info": {
"id": "my-unique-model-id",
"input_cost_per_token": 0.000006,
"output_cost_per_token": 0.00003,
"cache_creation_input_token_cost": 0.0000075,
"cache_read_input_token_cost": 0.0000006,
},
},
{
"model_name": "claude-3-5-sonnet-20240620",
"litellm_params": {
"model": "anthropic/claude-3-5-sonnet-20240620",
"api_key": "test_api_key",
},
"model_info": {
"input_cost_per_token": 100,
"output_cost_per_token": 200,
},
},
]
)
result = router.completion(
model="claude-3-5-sonnet-20240620",
messages=[{"role": "user", "content": "Hello, world!"}],
mock_response=True,
)
result_2 = router.completion(
model="prod/claude-3-5-sonnet-20240620",
messages=[{"role": "user", "content": "Hello, world!"}],
mock_response=True,
)
assert (
result._hidden_params["response_cost"]
> result_2._hidden_params["response_cost"]
)
model_info = router.get_deployment_model_info(
model_id="my-unique-model-id", model_name="anthropic/claude-3-5-sonnet-20240620"
)
assert model_info is not None
assert model_info["input_cost_per_token"] == 0.000006
assert model_info["output_cost_per_token"] == 0.00003
assert model_info["cache_creation_input_token_cost"] == 0.0000075
assert model_info["cache_read_input_token_cost"] == 0.0000006
def test_azure_realtime_cost_calculator():
from litellm import get_model_info
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
cost = handle_realtime_stream_cost_calculation(
results=[
{
"type": "session.created",
"session": {"model": "gpt-4o-realtime-preview-2024-12-17"},
},
],
combined_usage_object=Usage(
prompt_tokens=100,
completion_tokens=100,
prompt_tokens_details=PromptTokensDetailsWrapper(
text_tokens=10, audio_tokens=90
),
),
custom_llm_provider="azure",
litellm_model_name="my-custom-azure-deployment",
)
assert cost > 0
def test_default_image_cost_calculator(monkeypatch):
from litellm.cost_calculator import default_image_cost_calculator
temp_object = {
"litellm_provider": "azure",
"input_cost_per_pixel": 10,
}
monkeypatch.setattr(
litellm,
"model_cost",
{
"azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b": temp_object
},
)
args = {
"model": "azure/bf9001cd7209f5734ecb4ab937a5a0e2ba5f119708bd68f184db362930f9dc7b",
"custom_llm_provider": "azure",
"quality": "standard",
"n": 1,
"size": "1024-x-1024",
"optional_params": {},
}
cost = default_image_cost_calculator(**args)
assert cost == 10485760
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