diff --git a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py index 463a871c6cc..f1340df8b69 100644 --- a/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py +++ b/tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py @@ -410,6 +410,170 @@ def test_get_token_base_cost_picks_highest_crossed_tier(): assert prompt_base_cost == 9e-6 +def test_is_within_off_peak_window_same_day(): + from datetime import datetime, timezone + + window = "09:00-17:00" + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 8, 59, tzinfo=timezone.utc)) is False + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 17, 0, tzinfo=timezone.utc)) is False + + +def test_is_within_off_peak_window_wraps_midnight(): + from datetime import datetime, timezone + + window = "16:30-00:30" + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 15, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 16, 30, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 30, tzinfo=timezone.utc)) is False + assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is False + + +def test_is_within_off_peak_window_multiple_windows(): + from datetime import datetime, timezone + + # Providers like DeepSeek V4 have more than one daily peak/off-peak window. + windows = ["01:00-05:00", "13:00-16:00"] + assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 3, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 14, 30, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is False + # a malformed entry in the list is ignored, valid entries still match + assert _is_within_off_peak_window(["bad", "13:00-16:00"], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is True + assert _is_within_off_peak_window([], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is False + + +def test_is_within_off_peak_window_normalizes_timezone_aware_input(): + from datetime import datetime, timedelta, timezone + + # A caller may pass a non-UTC aware datetime; the window is UTC and must be + # evaluated in UTC, not against the caller's wall-clock. 09:00 at UTC+8 is + # 01:00 UTC, inside the 01:00-05:00 window. + tz_plus_8 = timezone(timedelta(hours=8)) + assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 9, 0, tzinfo=tz_plus_8)) is True + assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 12, 0, tzinfo=tz_plus_8)) is True + # 06:00 at UTC+8 is 22:00 UTC the previous day, outside the window + assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 6, 0, tzinfo=tz_plus_8)) is False + + +def test_is_within_off_peak_window_malformed_returns_false(): + from datetime import datetime, timezone + + now = datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc) + assert _is_within_off_peak_window("not-a-window", now) is False + assert _is_within_off_peak_window("16:30", now) is False + assert _is_within_off_peak_window("25:00-26:00", now) is False + + +def test_get_token_base_cost_applies_off_peak_pricing(): + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "cache_read_input_token_cost": 1e-7, + "off_peak_pricing": { + "hours_utc": "16:30-00:30", + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + "cache_read_input_token_cost": 5e-8, + }, + }, + ) + usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) + + off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) + assert off_peak[0] == 5e-7 + assert off_peak[1] == 1e-6 + assert off_peak[4] == 5e-8 + + peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) + assert peak[0] == 1e-6 + assert peak[1] == 2e-6 + assert peak[4] == 1e-7 + + +def test_get_token_base_cost_off_peak_falls_back_to_standard_when_unset(): + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "off_peak_pricing": {"hours_utc": "16:30-00:30", "input_cost_per_token": 5e-7}, + }, + ) + usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) + + result = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) + assert result[0] == 5e-7 + assert result[1] == 2e-6 + + +def test_get_token_base_cost_off_peak_wins_over_threshold(): + from datetime import datetime, timezone + from typing import cast + + from litellm.types.utils import ModelInfo + + model_info = cast( + ModelInfo, + { + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "input_cost_per_token_above_200k_tokens": 3e-6, + "output_cost_per_token_above_200k_tokens": 4e-6, + "off_peak_pricing": { + "hours_utc": "16:30-00:30", + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + }, + }, + ) + usage = Usage(prompt_tokens=250000, completion_tokens=250000, total_tokens=500000) + + off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) + assert off_peak[0] == 5e-7 + assert off_peak[1] == 1e-6 + + peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) + assert peak[0] == 3e-6 + assert peak[1] == 4e-6 + + +def test_get_model_info_propagates_off_peak_fields(): + model_name = "test-off-peak-model" + off_peak_pricing = { + "hours_utc": "16:30-00:30", + "input_cost_per_token": 5e-7, + "output_cost_per_token": 1e-6, + "cache_read_input_token_cost": 5e-8, + } + litellm.register_model( + { + model_name: { + "litellm_provider": "openai", + "mode": "chat", + "input_cost_per_token": 1e-6, + "output_cost_per_token": 2e-6, + "off_peak_pricing": off_peak_pricing, + } + } + ) + info = litellm.get_model_info(model=model_name) + assert info["off_peak_pricing"] == off_peak_pricing + + def test_generic_cost_per_token_gpt54_above_272k_tokens(_local_model_cost_map): """GPT-5.4/5.4-pro: prompts >272K input tokens priced at 2x input, 1.5x output.""" model = "gpt-5.4" @@ -3947,167 +4111,3 @@ def test_route_image_generation_cost_falls_back_to_requested_size(monkeypatch, r ) assert cost == expected_cost - - -def test_is_within_off_peak_window_same_day(): - from datetime import datetime, timezone - - window = "09:00-17:00" - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 8, 59, tzinfo=timezone.utc)) is False - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 17, 0, tzinfo=timezone.utc)) is False - - -def test_is_within_off_peak_window_wraps_midnight(): - from datetime import datetime, timezone - - window = "16:30-00:30" - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 15, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 16, 30, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 0, 30, tzinfo=timezone.utc)) is False - assert _is_within_off_peak_window(window, datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) is False - - -def test_is_within_off_peak_window_multiple_windows(): - from datetime import datetime, timezone - - # Providers like DeepSeek V4 have more than one daily peak/off-peak window. - windows = ["01:00-05:00", "13:00-16:00"] - assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 3, 0, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 14, 30, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window(windows, datetime(2026, 1, 1, 9, 0, tzinfo=timezone.utc)) is False - # a malformed entry in the list is ignored, valid entries still match - assert _is_within_off_peak_window(["bad", "13:00-16:00"], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is True - assert _is_within_off_peak_window([], datetime(2026, 1, 1, 14, 0, tzinfo=timezone.utc)) is False - - -def test_is_within_off_peak_window_normalizes_timezone_aware_input(): - from datetime import datetime, timedelta, timezone - - # A caller may pass a non-UTC aware datetime; the window is UTC and must be - # evaluated in UTC, not against the caller's wall-clock. 09:00 at UTC+8 is - # 01:00 UTC, inside the 01:00-05:00 window. - tz_plus_8 = timezone(timedelta(hours=8)) - assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 9, 0, tzinfo=tz_plus_8)) is True - assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 12, 0, tzinfo=tz_plus_8)) is True - # 06:00 at UTC+8 is 22:00 UTC the previous day, outside the window - assert _is_within_off_peak_window("01:00-05:00", datetime(2026, 1, 1, 6, 0, tzinfo=tz_plus_8)) is False - - -def test_is_within_off_peak_window_malformed_returns_false(): - from datetime import datetime, timezone - - now = datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc) - assert _is_within_off_peak_window("not-a-window", now) is False - assert _is_within_off_peak_window("16:30", now) is False - assert _is_within_off_peak_window("25:00-26:00", now) is False - - -def test_get_token_base_cost_applies_off_peak_pricing(): - from datetime import datetime, timezone - from typing import cast - - from litellm.types.utils import ModelInfo - - model_info = cast( - ModelInfo, - { - "input_cost_per_token": 1e-6, - "output_cost_per_token": 2e-6, - "cache_read_input_token_cost": 1e-7, - "off_peak_pricing": { - "hours_utc": "16:30-00:30", - "input_cost_per_token": 5e-7, - "output_cost_per_token": 1e-6, - "cache_read_input_token_cost": 5e-8, - }, - }, - ) - usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) - - off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) - assert off_peak[0] == 5e-7 - assert off_peak[1] == 1e-6 - assert off_peak[4] == 5e-8 - - peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) - assert peak[0] == 1e-6 - assert peak[1] == 2e-6 - assert peak[4] == 1e-7 - - -def test_get_token_base_cost_off_peak_falls_back_to_standard_when_unset(): - from datetime import datetime, timezone - from typing import cast - - from litellm.types.utils import ModelInfo - - model_info = cast( - ModelInfo, - { - "input_cost_per_token": 1e-6, - "output_cost_per_token": 2e-6, - "off_peak_pricing": {"hours_utc": "16:30-00:30", "input_cost_per_token": 5e-7}, - }, - ) - usage = Usage(prompt_tokens=100, completion_tokens=50, total_tokens=150) - - result = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) - assert result[0] == 5e-7 - assert result[1] == 2e-6 - - -def test_get_token_base_cost_off_peak_wins_over_threshold(): - from datetime import datetime, timezone - from typing import cast - - from litellm.types.utils import ModelInfo - - model_info = cast( - ModelInfo, - { - "input_cost_per_token": 1e-6, - "output_cost_per_token": 2e-6, - "input_cost_per_token_above_200k_tokens": 3e-6, - "output_cost_per_token_above_200k_tokens": 4e-6, - "off_peak_pricing": { - "hours_utc": "16:30-00:30", - "input_cost_per_token": 5e-7, - "output_cost_per_token": 1e-6, - }, - }, - ) - usage = Usage(prompt_tokens=250000, completion_tokens=250000, total_tokens=500000) - - off_peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 18, 0, tzinfo=timezone.utc)) - assert off_peak[0] == 5e-7 - assert off_peak[1] == 1e-6 - - peak = _get_token_base_cost(model_info, usage, current_time=datetime(2026, 1, 1, 12, 0, tzinfo=timezone.utc)) - assert peak[0] == 3e-6 - assert peak[1] == 4e-6 - - -def test_get_model_info_propagates_off_peak_fields(): - model_name = "test-off-peak-model" - off_peak_pricing = { - "hours_utc": "16:30-00:30", - "input_cost_per_token": 5e-7, - "output_cost_per_token": 1e-6, - "cache_read_input_token_cost": 5e-8, - } - litellm.register_model( - { - model_name: { - "litellm_provider": "openai", - "mode": "chat", - "input_cost_per_token": 1e-6, - "output_cost_per_token": 2e-6, - "off_peak_pricing": off_peak_pricing, - } - } - ) - info = litellm.get_model_info(model=model_name) - assert info["off_peak_pricing"] == off_peak_pricing