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cleveragents-core/benchmarks/providers_cost_table_bench.py
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fix: restore CI quality tests to passing state (#4175)
Co-authored-by: Jeffrey Phillips Freeman <the@jeffreyfreeman.me>
Co-committed-by: Jeffrey Phillips Freeman <the@jeffreyfreeman.me>
2026-04-08 11:02:14 +00:00

115 lines
4.2 KiB
Python

"""ASV benchmarks for ProviderCostTable construction and iteration.
Measures the performance of:
- ProviderCostTable construction with default entries
- ProviderCostTable construction with custom entries merged in
- Iterating all providers and their models (throughput)
- CostEntry.estimate_cost across the full default table
Note: Basic lookup benchmarks (get_cost_entry, list_providers, list_models)
are already covered by cost_controls_bench.py and are not duplicated here.
"""
from __future__ import annotations
import importlib
import sys
from pathlib import Path
_SRC = str(Path(__file__).resolve().parents[1] / "src")
if _SRC not in sys.path:
sys.path.insert(0, _SRC)
import cleveragents # noqa: E402
importlib.reload(cleveragents)
from cleveragents.providers.cost_table import CostEntry, ProviderCostTable # noqa: E402
# ---------------------------------------------------------------------------
# Shared fixtures
# ---------------------------------------------------------------------------
_CUSTOM_ENTRIES: dict[str, dict[str, CostEntry]] = {
"custom_provider": {
"custom-model-v1": CostEntry(
input_cost_per_token=0.000002,
output_cost_per_token=0.000008,
),
"custom-model-v2": CostEntry(
input_cost_per_token=0.000001,
output_cost_per_token=0.000004,
),
},
"openai": {
"gpt-5": CostEntry(
input_cost_per_token=0.00001,
output_cost_per_token=0.00003,
),
},
}
class TimeCostTableConstruction:
"""Benchmark ProviderCostTable construction cost."""
def time_construct_default(self) -> None:
"""Time construction of a cost table with only default entries."""
ProviderCostTable()
def time_construct_with_custom_entries(self) -> None:
"""Time construction of a cost table with custom entries merged in."""
ProviderCostTable(custom_entries=_CUSTOM_ENTRIES)
def time_construct_with_empty_custom(self) -> None:
"""Time construction with an empty custom entries dict (no-op merge)."""
ProviderCostTable(custom_entries={})
class TimeCostTableIteration:
"""Benchmark iterating all providers and models in the cost table."""
def setup(self) -> None:
self.table = ProviderCostTable()
self.table_with_custom = ProviderCostTable(custom_entries=_CUSTOM_ENTRIES)
def time_iterate_all_providers_and_models(self) -> None:
"""Time iterating every provider and every model in the default table."""
for provider in self.table.list_providers():
for model in self.table.list_models(provider):
self.table.get_cost_entry(provider, model)
def time_iterate_all_providers_and_models_custom(self) -> None:
"""Time iterating every provider and model in a table with custom entries."""
for provider in self.table_with_custom.list_providers():
for model in self.table_with_custom.list_models(provider):
self.table_with_custom.get_cost_entry(provider, model)
def time_estimate_cost_all_entries(self) -> None:
"""Time calling estimate_cost for every known provider/model pair."""
for provider in self.table.list_providers():
for model in self.table.list_models(provider):
entry = self.table.get_cost_entry(provider, model)
entry.estimate_cost(input_tokens=1000, output_tokens=500)
class TimeCostTableFallback:
"""Benchmark the fallback path for unknown provider/model combinations."""
def setup(self) -> None:
self.table = ProviderCostTable()
def time_lookup_unknown_provider(self) -> None:
"""Time lookup for a completely unknown provider (returns DEFAULT_COST)."""
self.table.get_cost_entry("nonexistent_provider", "nonexistent_model")
def time_lookup_known_provider_unknown_model(self) -> None:
"""Time lookup for a known provider but unknown model (returns DEFAULT_COST)."""
self.table.get_cost_entry("anthropic", "claude-99-ultra")
def time_default_cost_estimate(self) -> None:
"""Time estimating cost using the DEFAULT_COST fallback entry."""
ProviderCostTable.DEFAULT_COST.estimate_cost(
input_tokens=10_000, output_tokens=5_000
)