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test(cost): retarget pricing fixtures after the main map mirror

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This commit is contained in:
Joshua Valluru 2026-10-20 16:41:21 -07:00
parent 6a2253d458
commit 60eb7fedf3
1 changed files with 41 additions and 21 deletions
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62
tests/test_litellm/test_cost_calculator.py
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@ -152,6 +152,11 @@ def test_cost_calculator_with_response_cost_in_additional_headers():
def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch):
monkeypatch.setitem(
litellm.model_cost,
"gemini-3.5-flash",
{**litellm.model_cost["gemini-3.5-flash"], "input_cost_per_image_token": None},
)
usage = Usage(
prompt_tokens=120,
completion_tokens=100,
@ -161,7 +166,7 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch):
image_tokens=20,
),
)
mr = ModelResponse(usage=usage, model="gemini-2.0-flash-001")
mr = ModelResponse(usage=usage, model="gemini-3.5-flash")
result = response_cost_calculator(
response_object=mr,
@ -173,7 +178,7 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch):
base_model=None,
)
model_info = litellm.model_cost["gemini-2.0-flash-001"]
model_info = litellm.model_cost["gemini-3.5-flash"]
# Step 1: Test a model where input_cost_per_image_token is not set.
# In this case the calculation should use input_cost_per_token as fallback.
@ -198,7 +203,7 @@ def test_cost_calculator_with_usage(_local_model_cost_map, monkeypatch):
monkeypatch.setattr(
litellm,
"model_cost",
{"gemini-2.0-flash-001": temp_model_info_object},
{"gemini-3.5-flash": temp_model_info_object},
)
# Invalidate caches after modifying litellm.model_cost
@ -235,7 +240,7 @@ def test_handle_realtime_stream_cost_calculation_stores_cost_breakdown():
from litellm.litellm_core_utils.litellm_logging import Logging
results: OpenAIRealtimeStreamList = [
{"type": "session.created", "session": {"model": "gpt-4o-realtime-preview"}},
{"type": "session.created", "session": {"model": "gpt-realtime-2.1"}},
{
"type": "response.done",
"response": {
@ -252,7 +257,7 @@ def test_handle_realtime_stream_cost_calculation_stores_cost_breakdown():
)
logging_obj = Logging(
model="gpt-4o-realtime-preview",
model="gpt-realtime-2.1",
messages=[],
stream=False,
call_type="_arealtime",
@ -265,7 +270,7 @@ def test_handle_realtime_stream_cost_calculation_stores_cost_breakdown():
results=results,
combined_usage_object=combined_usage_object,
custom_llm_provider="openai",
litellm_model_name="gpt-4o-realtime-preview",
litellm_model_name="gpt-realtime-2.1",
litellm_logging_obj=logging_obj,
)
@ -748,9 +753,9 @@ def test_per_query_priced_rerank_deployment_completion_cost_is_nonzero():
router: Final = Router(
model_list=[
{
"model_name": "semantic-ranker-default-004",
"model_name": "semantic-ranker-custom-test",
"litellm_params": {
"model": "vertex_ai/semantic-ranker-default-004",
"model": "vertex_ai/semantic-ranker-custom-test",
"vertex_project": "test-project",
"vertex_location": "us-east5",
},
@ -759,7 +764,7 @@ def test_per_query_priced_rerank_deployment_completion_cost_is_nonzero():
]
)
router_model_id: Final = router.model_list[0]["model_info"]["id"]
assert litellm.model_cost["vertex_ai/semantic-ranker-default-004"].get("input_cost_per_query") is None
assert litellm.model_cost["vertex_ai/semantic-ranker-custom-test"].get("input_cost_per_query") is None
response: Final = RerankResponse(
id="vertex_ai_rerank_test",
@ -769,7 +774,7 @@ def test_per_query_priced_rerank_deployment_completion_cost_is_nonzero():
cost: Final = completion_cost(
completion_response=response,
model="vertex_ai/semantic-ranker-default-004",
model="vertex_ai/semantic-ranker-custom-test",
custom_llm_provider="vertex_ai",
call_type="arerank",
custom_pricing=True,
@ -1031,7 +1036,7 @@ def test_bedrock_cost_calculator_comparison_with_without_cache():
print(f"Cost with cache: {cost_with_cache}")
def test_log_context_cost_calculation():
def test_log_context_cost_calculation(monkeypatch: pytest.MonkeyPatch):
"""
Test that log context cost calculation works correctly with tiered pricing.
@ -1048,10 +1053,20 @@ def test_log_context_cost_calculation():
)
# Create a mock response with extended context usage
monkeypatch.setitem(
litellm.model_cost,
"claude-sonnet-5",
{
**litellm.model_cost["claude-sonnet-5"],
"input_cost_per_token_above_200k_tokens": 8e-6,
"output_cost_per_token_above_200k_tokens": 3e-5,
"cache_creation_input_token_cost_above_200k_tokens": 9e-6,
},
)
extended_context_response = ModelResponse(
id="test-extended-context-response",
created=1750733889,
model="claude-4-sonnet-20250514",
model="claude-sonnet-5",
object="chat.completion",
system_fingerprint=None,
choices=[
@ -1087,7 +1102,7 @@ def test_log_context_cost_calculation():
# Calculate the cost using the extended context model
result = completion_cost(
completion_response=extended_context_response,
model="claude-4-sonnet-20250514",
model="claude-sonnet-5",
custom_llm_provider="anthropic",
)
@ -1097,7 +1112,7 @@ def test_log_context_cost_calculation():
# Get model info to understand the pricing
from litellm import get_model_info
model_info = get_model_info(model="claude-4-sonnet-20250514", custom_llm_provider="anthropic")
model_info = get_model_info(model="claude-sonnet-5", custom_llm_provider="anthropic")
# Calculate expected cost based on actual model pricing
input_cost_per_token = model_info.get("input_cost_per_token", 0)
@ -1721,7 +1736,7 @@ def test_cost_margin_with_discount(monkeypatch):
print(f" - Expected: ${expected_cost:.6f}")
def test_azure_image_generation_cost_calculator():
def test_azure_image_generation_cost_calculator(monkeypatch: pytest.MonkeyPatch):
from unittest.mock import MagicMock
from litellm.types.utils import (
@ -1731,6 +1746,11 @@ def test_azure_image_generation_cost_calculator():
ImageUsageInputTokensDetails,
)
monkeypatch.setitem(
litellm.model_cost,
"hd/1024-x-1024/test-pixel-image",
{"input_cost_per_pixel": 1e-7, "litellm_provider": "azure", "mode": "image_generation"},
)
response_cost_calculator_kwargs = {
"response_object": ImageResponse(
created=1761785270,
@ -1752,10 +1772,10 @@ def test_azure_image_generation_cost_calculator():
total_tokens=0,
),
),
"model": "azure/dall-e-3",
"model": "azure/test-pixel-image",
"cache_hit": False,
"custom_llm_provider": "azure",
"base_model": "azure/dall-e-3",
"base_model": "azure/test-pixel-image",
"call_type": "aimage_generation",
"optional_params": {},
"custom_pricing": False,
@ -1770,7 +1790,7 @@ def test_azure_image_generation_cost_calculator():
}
cost = response_cost_calculator(**response_cost_calculator_kwargs)
assert cost > 0.079
assert cost == pytest.approx(1024 * 1024 * 1e-7)
def test_completion_cost_extracts_service_tier_from_response(_local_model_cost_map):
@ -2570,7 +2590,7 @@ def test_gemini_implicit_caching_cost_calculation():
# Verify cost calculation uses cached token pricing
response = ModelResponse(
id="mock-id",
model="gemini-2.0-flash",
model="gemini-3.5-flash",
choices=[
Choices(
index=0,
@ -2583,14 +2603,14 @@ def test_gemini_implicit_caching_cost_calculation():
cost = completion_cost(
completion_response=response,
model="gemini-2.0-flash",
model="gemini-3.5-flash",
custom_llm_provider="gemini",
)
# Get model pricing for verification
import litellm
model_info = litellm.get_model_info("gemini/gemini-2.0-flash")
model_info = litellm.get_model_info("gemini/gemini-3.5-flash")
input_cost = model_info.get("input_cost_per_token", 0)
cache_read_cost = model_info.get("cache_read_input_token_cost", input_cost)
output_cost = model_info.get("output_cost_per_token", 0)