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Merge 13d6d2accb into e9b1c11098

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5
litellm/__init__.py
View file

@ -689,6 +689,7 @@ openinfer_models: Final[Set[str]] = set()
kyma_models: Final[set[str]] = set() # mutable-ok: populated when the shared model catalog loads
streamlake_models: Final[Set[str]] = set()
wally_models: Final[Set[str]] = set()
apimodels_models: Final[Set[str]] = set()
zerogpu_models: Final[Set[str]] = set()
v0_models: Set = set()
morph_models: Set = set()
@ -987,6 +988,8 @@ def _populate_provider_model_sets(model_cost_map: Mapping[str, Mapping[str, obje
streamlake_models.add(key)
elif value.get("litellm_provider") == "wally":
wally_models.add(key)
elif value.get("litellm_provider") == "apimodels":
apimodels_models.add(key)
elif value.get("litellm_provider") == "zerogpu":
zerogpu_models.add(key)
elif value.get("litellm_provider") == "v0":
@ -1169,6 +1172,7 @@ model_list = list(
| kyma_models
| streamlake_models
| wally_models
| apimodels_models
| zerogpu_models
| v0_models
| morph_models
@ -1303,6 +1307,7 @@ def _build_models_by_provider() -> dict:
"kyma": kyma_models,
"streamlake": streamlake_models,
"wally": wally_models,
"apimodels": apimodels_models,
"zerogpu": zerogpu_models,
"v0": v0_models,
"morph": morph_models,

2
litellm/constants.py
View file

@ -1095,6 +1095,7 @@ openai_compatible_endpoints: Final[list[str]] = [
"https://api.prisminference.com/v1",
"https://api.reka.ai/v1",
"https://inference.runanywhere.ai/v1",
"https://api.apimodels.app/v1",
"https://gigachat.devices.sberbank.ru/api/v1",
"https://api.openinfer.ai/v1",
"https://vanchin.streamlake.ai/api/gateway/v1/endpoints",
@ -1183,6 +1184,7 @@ openai_compatible_providers: Final[list[str]] = [
"reka",
"streamlake",
"wally",
"apimodels",
]
OPENAI_AUDIO_TRANSCRIPTION_PROVIDERS: Final = frozenset({"openai"} | frozenset(openai_compatible_providers))

5
litellm/llms/openai_like/providers.json
View file

@ -314,5 +314,10 @@
"supports_function_calling": true,
"supports_reasoning": true
}
},
"apimodels": {
"base_url": "https://api.apimodels.app/v1",
"api_key_env": "APIMODELS_API_KEY",
"api_base_env": "APIMODELS_API_BASE"
}
}

637
litellm/model_prices_and_context_window_backup.json
View file

@ -74112,6 +74112,643 @@
"supports_tool_choice": true,
"supports_vision": true
},
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"litellm_provider": "apimodels",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 2.5e-05,
"source": "https://apimodels.app/models/claude-fable-5-1",
"supports_function_calling": true,
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},
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"litellm_provider": "apimodels",
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},
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},
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},
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26
litellm/provider_endpoints_support_backup.json
View file

@ -321,6 +321,32 @@
"generateContent": false
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"url": "https://docs.litellm.ai/docs/providers/aihubmix",

28
litellm/proxy/public_endpoints/provider_create_fields.json
View file

@ -3743,6 +3743,34 @@
],
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},
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{
"provider": "StreamLake",
"provider_display_name": "StreamLake",

1
litellm/types/utils.py
View file

@ -4800,6 +4800,7 @@ class LlmProviders(str, Enum):
REKA = "reka"
STREAMLAKE = "streamlake"
WALLY = "wally"
APIMODELS = "apimodels"
LITELLM_AGENT = "litellm_agent"
CURSOR = "cursor"
BEDROCK_MANTLE = "bedrock_mantle"

637
model_prices_and_context_window.json
View file

@ -74112,6 +74112,643 @@
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"apimodels/gpt-5.6-terra": {
"cache_read_input_token_cost": 5.5e-08,
"cache_read_input_token_cost_above_272k_tokens": 1.1e-07,
"input_cost_per_token": 5.51e-07,
"input_cost_per_token_above_272k_tokens": 1.102e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 922000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 3.309e-06,
"output_cost_per_token_above_272k_tokens": 4.9635e-06,
"source": "https://apimodels.app/models/gpt-5-6-terra",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/gpt-5.6-luna": {
"cache_read_input_token_cost": 1.8e-08,
"cache_read_input_token_cost_above_272k_tokens": 3.6e-08,
"input_cost_per_token": 1.8e-07,
"input_cost_per_token_above_272k_tokens": 3.6e-07,
"litellm_provider": "apimodels",
"max_input_tokens": 922000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 9.6e-07,
"output_cost_per_token_above_272k_tokens": 1.44e-06,
"source": "https://apimodels.app/models/gpt-5-6-luna",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/gpt-5-5": {
"cache_read_input_token_cost": 5e-07,
"input_cost_per_token": 3e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 1050000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1.8e-05,
"source": "https://apimodels.app/models/gpt-5-5",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/gpt-5-4": {
"cache_read_input_token_cost": 2.5e-07,
"input_cost_per_token": 1.8e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 1050000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1.08e-05,
"source": "https://apimodels.app/models/gpt-5-4",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/grok-4.6": {
"cache_read_input_token_cost": 3.75e-07,
"input_cost_per_token": 1.5e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 500000,
"max_output_tokens": 500000,
"mode": "chat",
"output_cost_per_token": 4.5e-06,
"source": "https://apimodels.app/models/grok-4.6",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/grok-4.5": {
"cache_read_input_token_cost": 3e-07,
"input_cost_per_token": 1.275e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 500000,
"max_output_tokens": 500000,
"mode": "chat",
"output_cost_per_token": 4.25e-06,
"source": "https://apimodels.app/models/grok-4.5",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/glm-5.3": {
"cache_read_input_token_cost": 2.47e-07,
"input_cost_per_token": 1.33e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 4.18e-06,
"source": "https://apimodels.app/models/glm-5.3",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true
},
"apimodels/glm-5.2": {
"cache_read_input_token_cost": 2.6e-07,
"input_cost_per_token": 1.26e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 3.96e-06,
"source": "https://apimodels.app/models/glm-5.2",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true
},
"apimodels/deepseek-v4.1-flash": {
"cache_read_input_token_cost": 5.4e-09,
"input_cost_per_token": 2.7e-07,
"litellm_provider": "apimodels",
"mode": "chat",
"off_peak_pricing": {
"cache_read_input_token_cost": 2.7e-09,
"input_cost_per_token": 1.35e-07,
"output_cost_per_token": 5.4e-07,
"windows": [
{
"hours_utc": [
"00:00-01:00",
"04:00-06:00",
"10:00-00:00"
],
"weekdays": [
1,
2,
3,
4,
5
]
},
{
"hours_utc": "00:00-00:00",
"weekdays": [
6,
7
]
}
]
},
"output_cost_per_token": 1.08e-06,
"source": "https://apimodels.app/models/deepseek-v4.1-flash"
},
"apimodels/deepseek-v4-flash": {
"cache_read_input_token_cost": 5.4e-09,
"input_cost_per_token": 2.7e-07,
"litellm_provider": "apimodels",
"max_input_tokens": 1000000,
"max_output_tokens": 393216,
"mode": "chat",
"off_peak_pricing": {
"cache_read_input_token_cost": 2.7e-09,
"input_cost_per_token": 1.35e-07,
"output_cost_per_token": 5.4e-07,
"windows": [
{
"hours_utc": [
"00:00-01:00",
"04:00-06:00",
"10:00-00:00"
],
"weekdays": [
1,
2,
3,
4,
5
]
},
{
"hours_utc": "00:00-00:00",
"weekdays": [
6,
7
]
}
]
},
"output_cost_per_token": 1.08e-06,
"source": "https://apimodels.app/models/deepseek-v4-flash",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true
},
"apimodels/qwen3.8-max": {
"cache_read_input_token_cost": 2.5e-07,
"input_cost_per_token": 2e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 991808,
"max_output_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 6e-06,
"source": "https://apimodels.app/models/qwen3.8-max",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true
},
"apimodels/qwen3.8-flash": {
"cache_read_input_token_cost": 1.6e-08,
"input_cost_per_token": 1.5e-07,
"litellm_provider": "apimodels",
"max_input_tokens": 991808,
"max_output_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 4.7e-07,
"source": "https://apimodels.app/models/qwen3.8-flash",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true
},
"apimodels/qwen3.7-max": {
"cache_read_input_token_cost": 4.5e-07,
"input_cost_per_token": 2.25e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 991808,
"max_output_tokens": 65536,
"mode": "chat",
"output_cost_per_token": 6.75e-06,
"source": "https://apimodels.app/models/qwen3.7-max",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true
},
"apimodels/qwen3.7-plus": {
"cache_read_input_token_cost": 7.2e-08,
"input_cost_per_token": 3.6e-07,
"litellm_provider": "apimodels",
"max_input_tokens": 991808,
"max_output_tokens": 65536,
"mode": "chat",
"output_cost_per_token": 1.44e-06,
"source": "https://apimodels.app/models/qwen3.7-plus",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"tiered_pricing": [
{
"cache_read_input_token_cost": 7.2e-08,
"input_cost_per_token": 3.6e-07,
"output_cost_per_token": 1.44e-06,
"range": [
0,
262144
]
},
{
"cache_read_input_token_cost": 2.16e-07,
"input_cost_per_token": 1.08e-06,
"output_cost_per_token": 4.32e-06,
"range": [
262144,
1000000
]
}
]
},
"bespoke/nimble-latest": {
"input_cost_per_token": 0.0,
"litellm_provider": "bespoke",

26
provider_endpoints_support.json
View file

@ -321,6 +321,32 @@
"generateContent": false
}
},
"apimodels": {
"display_name": "apimodels.app (`apimodels`)",
"url": "https://docs.litellm.ai/docs/providers/apimodels",
"endpoints": {
"chat_completions": true,
"responses": true,
"text_completion": true,
"embeddings": false,
"image_generations": false,
"image_edits": false,
"audio_transcriptions": false,
"audio_speech": false,
"rerank": false,
"ocr": false,
"search": false,
"moderations": false,
"realtime": false,
"video_generations": false,
"batches": false,
"messages": true,
"interactions": true,
"systemone": false,
"a2a": true,
"generateContent": true
}
},
"aihubmix": {
"display_name": "AIHubMix (`aihubmix`)",
"url": "https://docs.litellm.ai/docs/providers/aihubmix",

359
tests/unit/llms/openai_like/test_apimodels_provider.py Normal file
View file

@ -0,0 +1,359 @@
import json
from datetime import datetime, timezone
from pathlib import Path
from typing import Final
import pytest
import respx
import litellm
from litellm._internal_context import pinned_billing_time
from litellm.caching.llm_caching_handler import LLMClientCache
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
_DEFAULT_API_BASE: Final = "https://api.apimodels.app/v1"
_CHAT_COMPLETIONS_URL: Final = f"{_DEFAULT_API_BASE}/chat/completions"
_EXPECTED_ENDPOINTS: Final = {
"chat_completions": True,
"messages": True,
"responses": True,
"embeddings": False,
"image_generations": False,
"audio_transcriptions": False,
"audio_speech": False,
"moderations": False,
"batches": False,
"rerank": False,
}
# A Tuesday inside the DeepSeek weekday peak hours, and two moments outside them.
_WEEKDAY_PEAK: Final = datetime(2026, 11, 3, 2, 0, tzinfo=timezone.utc)
_WEEKDAY_OFF_PEAK: Final = datetime(2026, 11, 3, 12, 0, tzinfo=timezone.utc)
_WEEKEND: Final = datetime(2026, 11, 7, 2, 0, tzinfo=timezone.utc)
APIMODELS_MODELS: Final = tuple(sorted(name for name in litellm.model_cost if name.startswith("apimodels/")))
OFF_PEAK_MODELS: Final = tuple(name for name in APIMODELS_MODELS if "off_peak_pricing" in litellm.model_cost[name])
def _chat_completion(model: str) -> dict:
return {
"id": "chatcmpl_apimodels",
"object": "chat.completion",
"created": 1_790_000_000,
"model": model,
"choices": [
{
"index": 0,
"message": {"role": "assistant", "content": "Hello from APIMODELS"},
"finish_reason": "stop",
}
],
"usage": {"prompt_tokens": 4, "completion_tokens": 3, "total_tokens": 7},
}
def test_apimodels_is_a_registered_provider():
assert litellm.LlmProviders.APIMODELS.value == "apimodels"
assert "apimodels" in litellm.provider_list
assert "apimodels" in litellm.constants.openai_compatible_providers
assert _DEFAULT_API_BASE in litellm.constants.openai_compatible_endpoints
def test_apimodels_provider_resolution(monkeypatch: pytest.MonkeyPatch):
monkeypatch.setenv("APIMODELS_API_KEY", "apimodels-test-key")
monkeypatch.delenv("APIMODELS_API_BASE", raising=False)
model, provider, api_key, api_base = get_llm_provider(
model="apimodels/gpt-5.6-luna",
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert model == "gpt-5.6-luna"
assert provider == "apimodels"
assert api_key == "apimodels-test-key"
assert api_base == _DEFAULT_API_BASE
def test_apimodels_provider_keeps_explicit_credentials(monkeypatch: pytest.MonkeyPatch):
monkeypatch.setenv("APIMODELS_API_KEY", "apimodels-env-key")
monkeypatch.setenv("APIMODELS_API_BASE", "https://apimodels.env.example/v1")
_, provider, api_key, api_base = get_llm_provider(
model="apimodels/gpt-5.6-luna",
custom_llm_provider=None,
api_base="https://apimodels.internal.example/v1",
api_key="apimodels-explicit-key",
)
assert provider == "apimodels"
assert api_key == "apimodels-explicit-key"
assert api_base == "https://apimodels.internal.example/v1"
def test_apimodels_api_base_env_overrides_default(monkeypatch: pytest.MonkeyPatch):
monkeypatch.setenv("APIMODELS_API_KEY", "apimodels-env-key")
monkeypatch.setenv("APIMODELS_API_BASE", "https://apimodels.env.example/v1")
_, provider, _, api_base = get_llm_provider(
model="apimodels/gpt-5.6-luna",
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert provider == "apimodels"
assert api_base == "https://apimodels.env.example/v1"
def test_apimodels_api_base_autodetects_provider(monkeypatch: pytest.MonkeyPatch):
monkeypatch.setenv("APIMODELS_API_KEY", "apimodels-env-key")
model, provider, api_key, api_base = get_llm_provider(
model="gpt-5.6-luna",
custom_llm_provider=None,
api_base=_DEFAULT_API_BASE,
api_key=None,
)
assert model == "gpt-5.6-luna"
assert provider == "apimodels"
assert api_key == "apimodels-env-key"
assert api_base == _DEFAULT_API_BASE
@pytest.mark.parametrize("model", APIMODELS_MODELS)
def test_apimodels_model_cost_and_capabilities(model: str):
from litellm.cost_calculator import cost_per_token
from litellm.utils import supports_function_calling, supports_prompt_caching, supports_reasoning, supports_vision
entry: Final = litellm.model_cost[model]
with pinned_billing_time(_WEEKDAY_PEAK):
prompt_cost, completion_cost = cost_per_token(
model=model,
prompt_tokens=1_000,
completion_tokens=1_000,
custom_llm_provider="apimodels",
)
cached_prompt_cost, _ = cost_per_token(
model=model,
prompt_tokens=1_000,
completion_tokens=0,
cache_read_input_tokens=1_000,
custom_llm_provider="apimodels",
)
model_info: Final = litellm.get_model_info(model)
assert entry["source"].startswith("https://apimodels.app/models/")
assert entry["cache_read_input_token_cost"] < entry["input_cost_per_token"]
assert prompt_cost == pytest.approx(entry["input_cost_per_token"] * 1_000)
assert completion_cost == pytest.approx(entry["output_cost_per_token"] * 1_000)
assert cached_prompt_cost == pytest.approx(entry["cache_read_input_token_cost"] * 1_000)
assert model_info["litellm_provider"] == "apimodels"
assert "max_tokens" not in entry
assert model_info["max_tokens"] == entry.get("max_output_tokens")
declared_capabilities: Final = {
flag: check(model)
for flag, check in (
("supports_function_calling", supports_function_calling),
("supports_reasoning", supports_reasoning),
("supports_prompt_caching", supports_prompt_caching),
("supports_vision", supports_vision),
)
if flag in entry
}
assert declared_capabilities == {flag: entry[flag] for flag in declared_capabilities}
@pytest.mark.usefixtures("local_model_cost_map")
def test_apimodels_wildcard_expands_to_every_apimodels_row():
from litellm.proxy.auth.model_checks import get_known_models_from_wildcard
assert set(get_known_models_from_wildcard("apimodels/*")) == set(APIMODELS_MODELS)
def test_apimodels_backup_registry_mirrors_cost_map():
package_root: Final = Path(litellm.__file__).parent
cost_map: Final = json.loads((package_root.parent / "model_prices_and_context_window.json").read_text())
backup: Final = json.loads((package_root / "model_prices_and_context_window_backup.json").read_text())
apimodels_entries: Final = {name: entry for name, entry in cost_map.items() if name.startswith("apimodels/")}
assert apimodels_entries
assert tuple(sorted(apimodels_entries)) == APIMODELS_MODELS
assert apimodels_entries == {name: backup[name] for name in apimodels_entries}
def test_apimodels_is_available_in_add_model_form():
fields_path: Final = Path(litellm.__file__).parent / "proxy" / "public_endpoints" / "provider_create_fields.json"
providers: Final = json.loads(fields_path.read_text())
apimodels: Final = next(provider for provider in providers if provider["litellm_provider"] == "apimodels")
assert apimodels["provider"] == "APIMODELS"
assert apimodels["provider_display_name"] == "apimodels.app"
assert apimodels["default_model_placeholder"] in APIMODELS_MODELS
assert {field["key"]: field["required"] for field in apimodels["credential_fields"]} == {
"api_base": False,
"api_key": True,
}
def test_apimodels_supported_endpoints():
backup_path: Final = Path(litellm.__file__).parent / "provider_endpoints_support_backup.json"
root_path: Final = Path(litellm.__file__).parent.parent / "provider_endpoints_support.json"
backup: Final = json.loads(backup_path.read_text())["providers"]["apimodels"]["endpoints"]
root: Final = json.loads(root_path.read_text())["providers"]["apimodels"]["endpoints"]
assert backup == root
assert {endpoint: root[endpoint] for endpoint in _EXPECTED_ENDPOINTS} == _EXPECTED_ENDPOINTS
def test_apimodels_chat_completion_request_uses_env_api_key(monkeypatch: pytest.MonkeyPatch):
monkeypatch.setenv("APIMODELS_API_KEY", "apimodels-env-key")
monkeypatch.delenv("APIMODELS_API_BASE", raising=False)
with respx.mock() as upstream:
route: Final = upstream.post(_CHAT_COMPLETIONS_URL).respond(200, json=_chat_completion("gpt-5.6-luna"))
response: Final = litellm.completion(
model="apimodels/gpt-5.6-luna",
messages=[{"role": "user", "content": "Say hello"}],
)
request: Final = route.calls.last.request
body: Final = json.loads(request.content)
assert route.call_count == 1
assert str(request.url) == _CHAT_COMPLETIONS_URL
assert request.headers["authorization"] == "Bearer apimodels-env-key"
assert body["model"] == "gpt-5.6-luna"
assert body["messages"] == [{"role": "user", "content": "Say hello"}]
assert response.choices[0].message.content == "Hello from APIMODELS"
def test_apimodels_chat_completion_request_honours_api_base_override():
with respx.mock() as upstream:
route: Final = upstream.post("https://apimodels.internal.example/v1/chat/completions").respond(
200, json=_chat_completion("gpt-5.6-luna")
)
response: Final = litellm.completion(
model="apimodels/gpt-5.6-luna",
messages=[{"role": "user", "content": "Say hello"}],
api_base="https://apimodels.internal.example/v1",
api_key="apimodels-explicit-key",
)
request: Final = route.calls.last.request
assert route.call_count == 1
assert str(request.url) == "https://apimodels.internal.example/v1/chat/completions"
assert request.headers["authorization"] == "Bearer apimodels-explicit-key"
assert response.choices[0].message.content == "Hello from APIMODELS"
def test_apimodels_responses_request_is_bridged_to_chat_completions():
with respx.mock() as upstream:
route: Final = upstream.post(_CHAT_COMPLETIONS_URL).respond(200, json=_chat_completion("claude-opus-5-5"))
response: Final = litellm.responses(
model="apimodels/claude-opus-5-5",
input="Say hello",
api_key="apimodels-test-key",
)
request: Final = route.calls.last.request
body: Final = json.loads(request.content)
assert route.call_count == 1
assert request.headers["authorization"] == "Bearer apimodels-test-key"
assert body["model"] == "claude-opus-5-5"
assert body["messages"] == [{"role": "user", "content": "Say hello"}]
message_items: Final = [item for item in response.output if item.type == "message"]
assert message_items[0].content[0].text == "Hello from APIMODELS"
@pytest.mark.asyncio
async def test_apimodels_anthropic_messages_request_is_bridged_to_chat_completions(monkeypatch: pytest.MonkeyPatch):
monkeypatch.setattr(litellm, "disable_aiohttp_transport", True)
monkeypatch.setattr(litellm, "in_memory_llm_clients_cache", LLMClientCache())
with respx.mock() as upstream:
route: Final = upstream.post(_CHAT_COMPLETIONS_URL).respond(200, json=_chat_completion("gpt-5.6-luna"))
response: Final = await litellm.anthropic.messages.acreate(
model="apimodels/gpt-5.6-luna",
messages=[{"role": "user", "content": "Say hello"}],
max_tokens=32,
api_key="apimodels-test-key",
)
request: Final = route.calls.last.request
body: Final = json.loads(request.content)
assert route.call_count == 1
assert request.headers["authorization"] == "Bearer apimodels-test-key"
assert body["model"] == "gpt-5.6-luna"
assert body["messages"] == [{"role": "user", "content": "Say hello"}]
assert body["max_tokens"] == 32
assert any(block.get("text") == "Hello from APIMODELS" for block in response["content"])
def _completion_with_usage(model: str, usage: dict[str, object]) -> litellm.ModelResponse:
body: Final = {**_chat_completion(model), "usage": usage}
with respx.mock() as upstream:
upstream.post(_CHAT_COMPLETIONS_URL).respond(200, json=body)
return litellm.completion(
model=f"apimodels/{model}",
messages=[{"role": "user", "content": "Say hello"}],
api_key="apimodels-test-key",
)
def test_apimodels_chat_completion_cost_bills_cached_tokens_at_cache_read_rate():
row: Final = litellm.model_cost["apimodels/gpt-6-sol"]
response: Final = _completion_with_usage(
"gpt-6-sol",
{
"prompt_tokens": 1000,
"completion_tokens": 20,
"total_tokens": 1020,
"prompt_tokens_details": {"cached_tokens": 800},
},
)
assert row["cache_read_input_token_cost"] < row["input_cost_per_token"]
assert litellm.completion_cost(completion_response=response, model="apimodels/gpt-6-sol") == pytest.approx(
200 * row["input_cost_per_token"] + 800 * row["cache_read_input_token_cost"] + 20 * row["output_cost_per_token"]
)
def test_apimodels_prompt_over_272k_bills_the_whole_request_at_the_long_context_rates():
row: Final = litellm.model_cost["apimodels/gpt-6-sol"]
response: Final = _completion_with_usage(
"gpt-6-sol",
{
"prompt_tokens": 300_000,
"completion_tokens": 20,
"total_tokens": 300_020,
"prompt_tokens_details": {"cached_tokens": 100_000},
},
)
assert row["input_cost_per_token_above_272k_tokens"] > row["input_cost_per_token"]
assert litellm.completion_cost(completion_response=response, model="apimodels/gpt-6-sol") == pytest.approx(
200_000 * row["input_cost_per_token_above_272k_tokens"]
+ 100_000 * row["cache_read_input_token_cost_above_272k_tokens"]
+ 20 * row["output_cost_per_token_above_272k_tokens"]
)
@pytest.mark.parametrize("model", OFF_PEAK_MODELS)
@pytest.mark.parametrize("moment", (_WEEKDAY_OFF_PEAK, _WEEKEND), ids=("weekday-off-peak", "weekend"))
def test_apimodels_deepseek_rows_bill_half_rate_outside_weekday_peak_hours(model: str, moment: datetime):
from litellm.cost_calculator import cost_per_token
entry: Final = litellm.model_cost[model]
with pinned_billing_time(moment):
prompt_cost, completion_cost = cost_per_token(
model=model,
prompt_tokens=1_000,
completion_tokens=1_000,
custom_llm_provider="apimodels",
)
assert prompt_cost == pytest.approx(entry["input_cost_per_token"] / 2 * 1_000)
assert completion_cost == pytest.approx(entry["output_cost_per_token"] / 2 * 1_000)
def test_apimodels_off_peak_rows_are_the_deepseek_flash_models():
assert OFF_PEAK_MODELS == ("apimodels/deepseek-v4-flash", "apimodels/deepseek-v4.1-flash")