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Update test_llm_cost_calc_utils.py

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Praveen11558 2026-03-23 20:52:10 +05:30 • committed by GitHub
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1 changed files with 17 additions and 8 deletions
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tests/test_litellm/litellm_core_utils/llm_cost_calc/test_llm_cost_calc_utils.py
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@ -1430,8 +1430,8 @@ def test_image_count_billing_does_not_fill_prompt_token_gap():
f"Gap should not be filled when image_count billing is active."
)
assert completion_cost == pytest.approx(expected_completion_cost)
def test_character_count_billing_does_not_fill_prompt_token_gap():
"""
Regression: when character_count pricing is active, gaps between
@ -1441,6 +1441,11 @@ def test_character_count_billing_does_not_fill_prompt_token_gap():
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
# multimodalembedding@001 has non-zero input_cost_per_character (2e-07),
# input_cost_per_token (8e-07), and output_cost_per_token (0) in the cost map,
# making the character_count billing assertion non-trivial.
model = "multimodalembedding@001"
usage = Usage(
prompt_tokens=200,
completion_tokens=20,
@ -1455,17 +1460,17 @@ def test_character_count_billing_does_not_fill_prompt_token_gap():
)
prompt_cost, completion_cost = generic_cost_per_token(
model="gemini-2.0-flash-001",
model=model,
usage=usage,
custom_llm_provider="vertex_ai",
)
model_info = litellm.model_cost["gemini-2.0-flash-001"]
model_info = litellm.model_cost[model]
expected_prompt_cost = (
100 * model_info["input_cost_per_token"]
100 * model_info.get("input_cost_per_token", 0)
+ 1000 * model_info.get("input_cost_per_character", 0)
)
expected_completion_cost = 20 * model_info["output_cost_per_token"]
expected_completion_cost = 20 * model_info.get("output_cost_per_token", 0)
assert prompt_cost == pytest.approx(expected_prompt_cost), (
f"Expected prompt_cost={expected_prompt_cost}, got {prompt_cost}. "
@ -1483,6 +1488,10 @@ def test_video_length_billing_does_not_fill_prompt_token_gap():
os.environ["LITELLM_LOCAL_MODEL_COST_MAP"] = "True"
litellm.model_cost = litellm.get_model_cost_map(url="")
# multimodalembedding@001 has non-zero input_cost_per_video_per_second (0.0005)
# and input_cost_per_token (8e-07), making the video billing assertion non-trivial.
model = "multimodalembedding@001"
usage = Usage(
prompt_tokens=150,
completion_tokens=10,
@ -1497,12 +1506,12 @@ def test_video_length_billing_does_not_fill_prompt_token_gap():
)
prompt_cost, completion_cost = generic_cost_per_token(
model="gemini-2.0-flash-001",
model=model,
usage=usage,
custom_llm_provider="vertex_ai",
)
model_info = litellm.model_cost["gemini-2.0-flash-001"]
model_info = litellm.model_cost[model]
expected_prompt_cost = (
50 * model_info.get("input_cost_per_token", 0)
+ 12.0 * model_info.get("input_cost_per_video_per_second", 0)