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chore(tests): drop added registry tests, keep published dbu rates

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Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
This commit is contained in:
Devin AI 2026-08-26 22:30:20 +00:00
parent eef22e57a4
commit 984f38d1a5
2 changed files with 1 additions and 61 deletions
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32
tests/test_litellm/llms/databricks/test_databricks_cost_calculator.py
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@ -64,10 +64,6 @@ PUBLISHED_DBU_PER_MILLION: Final = {
"databricks/databricks-kimi-k3": ("42.857", "214.286", "42.857", "4.286"),
"databricks/databricks-glm-5-2": ("20.000", "62.857", "20.000", "3.714"),
}
MILLION_TOKEN_CONTEXT_MODELS: Final = (
"databricks/databricks-kimi-k3",
"databricks/databricks-glm-5-2",
)
PROMOTIONAL_DISCOUNT: Final = 0.80
PROMOTION_EXPIRES: Final = "2027-01-31"
ENTRIES_STORING_PROMOTIONAL_RATE: Final = (
@ -217,33 +213,7 @@ def test_every_model_without_published_cache_dbu_bills_cache_at_its_own_input_ra
assert info[field] == pytest.approx(info["input_cost_per_token"]), (model, field)
@pytest.mark.parametrize("model", MILLION_TOKEN_CONTEXT_MODELS)
def test_million_token_context_models_price_and_size_at_published_values(
local_model_cost_map: None,
model: str,
) -> None:
info: Final = _model_info(model)
input_dbu, output_dbu, _, cache_read_dbu = PUBLISHED_DBU_PER_MILLION[model]
assert info["input_cost_per_token"] == _dollars_per_token(input_dbu)
assert info["output_cost_per_token"] == _dollars_per_token(output_dbu)
assert info["cache_read_input_token_cost"] == _dollars_per_token(cache_read_dbu)
assert info["max_input_tokens"] == 1000000
assert info["mode"] == "chat"
assert info["supports_prompt_caching"] is True
def test_output_ceilings_match_what_each_vendor_publishes(local_model_cost_map: None) -> None:
assert _model_info("databricks/databricks-kimi-k3")["max_output_tokens"] == 1048576
assert _model_info("databricks/databricks-glm-5-2")["max_output_tokens"] == 131072
def test_kimi_k3_accepts_images_while_glm_5_2_is_text_only(local_model_cost_map: None) -> None:
assert _model_info("databricks/databricks-kimi-k3")["supports_vision"] is True
assert _model_info("databricks/databricks-glm-5-2")["supports_vision"] is False
@pytest.mark.parametrize("model", NEW_MODELS + MILLION_TOKEN_CONTEXT_MODELS)
@pytest.mark.parametrize("model", NEW_MODELS)
def test_backup_price_map_matches_main(model: str) -> None:
main_cost: Final = json.loads(MAIN_PRICES.read_text())
backup_cost: Final = json.loads(BACKUP_PRICES.read_text())

30
tests/test_litellm/llms/zai/test_zai_provider.py
View file

@ -128,36 +128,6 @@ def test_glm47_cost_calculation(local_model_cost_map):
assert math.isclose(completion_cost, 2.2, rel_tol=1e-6)
def test_glm53_context_and_capabilities(local_model_cost_map):
"""GLM-5.3 ships a 1M context with 128K max output, text in, reasoning always on"""
info = litellm.model_cost["zai/glm-5.3"]
assert info["max_input_tokens"] == 1000000
assert info["max_output_tokens"] == 128000
assert info["supports_reasoning"] is True
assert info["supports_prompt_caching"] is True
assert info.get("supports_vision") is not True
def test_glm53_cost_calculation(local_model_cost_map):
"""GLM-5.3 bills $1.4/M input, $4.4/M output, $0.26/M on a cache hit"""
prompt_cost, completion_cost = cost_per_token(
model="zai/glm-5.3",
prompt_tokens=1000000,
completion_tokens=1000000,
)
assert math.isclose(prompt_cost, 1.4, rel_tol=1e-6)
assert math.isclose(completion_cost, 4.4, rel_tol=1e-6)
assert math.isclose(
litellm.model_cost["zai/glm-5.3"]["cache_read_input_token_cost"] * 1000000,
0.26,
rel_tol=1e-6,
)
@pytest.mark.asyncio
async def test_zai_completion_call(respx_mock, zai_response, monkeypatch):
"""Test completion call with zai provider using mocked response"""