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test: read cost-map keys the implementation resolves and isolate the tariff test's model_cost copy

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
kerry 2026-09-17 22:32:39 +00:00
parent 6858663fd2
commit 9eb6fbc572
4 changed files with 14 additions and 16 deletions
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2
tests/test_litellm/llms/parallel_ai/test_parallel_ai_search.py
View file

@ -466,7 +466,7 @@ class TestParallelAISearch:
)
rate: Final = litellm.model_cost[
"parallel_ai/search-fast" if mode in ("fast", "turbo") else "parallel_ai/search"
{"fast": "parallel_ai/search-fast", "turbo": "parallel_ai/search-turbo"}.get(mode, "parallel_ai/search")
]["input_cost_per_query"]
request_count: Final = (
sum(item["count"] for item in usage if item["name"] == "sku_search") if usage is not None else 1

21
tests/test_litellm/proxy/common_utils/test_prompt_cache_pricing.py
View file

@ -1,4 +1,5 @@
from collections.abc import Mapping
from copy import deepcopy
from typing import Final
import pytest
@ -8,27 +9,23 @@ from litellm.proxy.common_utils.prompt_cache_pricing import price_cache_tokens
from litellm.types.management_endpoints.prompt_cache_prediction import CacheTokenBuckets
def _tiered_rate(entry: Mapping[str, float], field: str, total: int) -> float:
above_field: Final = f"{field}_above_200k_tokens"
if total > 200_000 and above_field in entry:
return entry[above_field]
return entry[field]
def _tiered_rate(entry: Mapping[str, float | None], field: str, total: int) -> float:
above_rate: Final = entry.get(f"{field}_above_200k_tokens") if total > 200_000 else None
rate: Final = above_rate if above_rate is not None else entry[field]
assert rate is not None
return rate
def _expected_cache_cost(model: str, tokens: CacheTokenBuckets) -> float:
key: Final = litellm.get_model_info(model=model, custom_llm_provider="anthropic")["key"]
entry: Final = litellm.model_cost[key]
total: Final = tokens.total_tokens
one_hour_field: Final = (
"cache_creation_input_token_cost_above_1hr_above_200k_tokens"
if total > 200_000 and "cache_creation_input_token_cost_above_1hr_above_200k_tokens" in entry
else "cache_creation_input_token_cost_above_1hr"
)
return (
tokens.uncached_input_tokens * _tiered_rate(entry, "input_cost_per_token", total)
+ tokens.cache_read_input_tokens * _tiered_rate(entry, "cache_read_input_token_cost", total)
+ tokens.cache_creation_5m_input_tokens * _tiered_rate(entry, "cache_creation_input_token_cost", total)
+ tokens.cache_creation_1h_input_tokens * entry[one_hour_field]
+ tokens.cache_creation_1h_input_tokens
* _tiered_rate(entry, "cache_creation_input_token_cost_above_1hr", total)
)
@ -58,7 +55,7 @@ def test_long_context_tier_starts_above_threshold(total: int) -> None:
def test_deployment_tariff_wins_without_proxy_discounts_or_margins(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setattr(litellm, "model_cost", litellm.model_cost.copy())
monkeypatch.setattr(litellm, "model_cost", deepcopy(litellm.model_cost))
litellm.Router(
model_list=[
{

5
tests/test_litellm/proxy/test_proxy_utils.py
View file

@ -2239,8 +2239,9 @@ def test_create_model_info_response_falls_back_to_alias_for_opaque_deployment_na
litellm.model_cost.clear()
litellm.model_cost.update(saved_model_cost)
assert response["max_input_tokens"] == 128000
assert response["max_output_tokens"] == 16384
entry: Final = litellm.model_cost["gpt-4o"]
assert response["max_input_tokens"] == entry["max_input_tokens"]
assert response["max_output_tokens"] == entry["max_output_tokens"]
def test_create_model_info_response_resolves_mode_through_deployment_model():

2
tests/test_litellm/test_cost_calculator.py
View file

@ -1346,7 +1346,7 @@ def test_gemini_25_implicit_caching_cost():
model="gemini/gemini-2.5-flash",
)
model_info: Final = litellm.model_cost["gemini-2.5-flash"]
model_info: Final = litellm.model_cost["gemini/gemini-2.5-flash"]
expected_cost = (
14316 * model_info["cache_read_input_token_cost"]
+ (15033 - 14316) * model_info["input_cost_per_token"]