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feat: add apimodels.app as an OpenAI-compatible provider

Browse source 
Registers apimodels as a JSON-configured OpenAI-compatible provider with
APIMODELS_API_KEY and APIMODELS_API_BASE, defaulting to
https://api.apimodels.app/v1. Adds it to LlmProviders, the
openai-compatible provider and endpoint lists, the Admin UI Add Model
form, the endpoint support matrix, and cost map rows for apimodels.app
chat models.

This is the earlier branch replayed onto current main as one commit.

Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
This commit is contained in:
stimQQ 2026-11-05 18:24:51 +09:00
parent 7e4ad6eb33
commit dfc68dbe93
9 changed files with 1627 additions and 0 deletions
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2
litellm/constants.py
View file

@ -1094,6 +1094,7 @@ 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",
@ -1182,6 +1183,7 @@ 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"
}
}

640
litellm/model_prices_and_context_window_backup.json
View file

@ -73944,6 +73944,646 @@
"supports_tool_choice": true,
"supports_vision": true
},
"apimodels/claude-fable-5-1": {
"cache_creation_input_token_cost": 5.778e-06,
"cache_read_input_token_cost": 2.2e-07,
"input_cost_per_token": 5e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 2.5e-05,
"source": "https://apimodels.app/models/claude-fable-5-1",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/claude-fable-5": {
"cache_creation_input_token_cost": 8.824e-06,
"cache_read_input_token_cost": 8.82e-07,
"input_cost_per_token": 5e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 2.5e-05,
"source": "https://apimodels.app/models/claude-fable-5",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
"apimodels/claude-opus-5-5": {
"cache_creation_input_token_cost": 3e-06,
"cache_read_input_token_cost": 1.2e-07,
"input_cost_per_token": 2.4e-06,
"litellm_provider": "apimodels",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1.2e-05,
"source": "https://apimodels.app/models/claude-opus-5-5",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
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"input_cost_per_token": 3e-06,
"litellm_provider": "apimodels",
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"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1.5e-05,
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},
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"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1.3e-05,
"source": "https://apimodels.app/models/claude-opus-4-8",
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"supports_reasoning": true,
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},
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"mode": "chat",
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"source": "https://apimodels.app/models/claude-opus-4-7",
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"supports_reasoning": true,
"supports_vision": true
},
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"litellm_provider": "apimodels",
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"max_tokens": 128000,
"mode": "chat",
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"source": "https://apimodels.app/models/claude-opus-4-6",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
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"mode": "chat",
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"source": "https://apimodels.app/models/claude-opus-4-5-20251101",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_vision": true
},
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"input_cost_per_token": 1.2e-06,
"litellm_provider": "apimodels",
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"mode": "chat",
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"source": "https://apimodels.app/models/claude-sonnet-5-5",
"supports_function_calling": true,
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"supports_vision": true
},
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"litellm_provider": "apimodels",
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"mode": "chat",
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"source": "https://apimodels.app/models/claude-sonnet-5",
"supports_function_calling": true,
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},
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},
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},
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},
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"supports_function_calling": true,
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"supports_reasoning": true,
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},
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},
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},
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},
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},
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},
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},
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},
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"source": "https://apimodels.app/models/glm-5.2",
"supports_function_calling": true,
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},
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"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"
},
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18
litellm/provider_endpoints_support_backup.json
View file

@ -347,6 +347,24 @@
"generateContent": true
}
},
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"url": "https://docs.litellm.ai/docs/pass_through/assembly_ai",

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

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

1
litellm/types/utils.py
View file

@ -4781,6 +4781,7 @@ class LlmProviders(str, Enum):
REKA = "reka"
STREAMLAKE = "streamlake"
WALLY = "wally"
APIMODELS = "apimodels"
LITELLM_AGENT = "litellm_agent"
CURSOR = "cursor"
BEDROCK_MANTLE = "bedrock_mantle"

640
model_prices_and_context_window.json
View file

@ -73944,6 +73944,646 @@
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"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,
"max_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,
"max_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,
"max_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,
"max_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,
"max_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,
"max_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,
"max_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,
"max_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,
"max_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",

18
provider_endpoints_support.json
View file

@ -321,6 +321,24 @@
"generateContent": false
}
},
"apimodels": {
"display_name": "apimodels.app (`apimodels`)",
"url": "https://docs.litellm.ai/docs/providers/apimodels",
"endpoints": {
"chat_completions": true,
"messages": true,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false,
"interactions": false
}
},
"aihubmix": {
"display_name": "AIHubMix (`aihubmix`)",
"url": "https://docs.litellm.ai/docs/providers/aihubmix",

275
tests/unit/llms/openai_like/test_apimodels_provider.py Normal file
View file

@ -0,0 +1,275 @@
import json
from pathlib import Path
from typing import Final
import pytest
import respx
import litellm
from litellm.caching.llm_caching_handler import LLMClientCache
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
def test_apimodels_provider_resolution(monkeypatch: pytest.MonkeyPatch):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
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 == "https://api.apimodels.app/v1"
def test_apimodels_provider_keeps_explicit_credentials(monkeypatch: pytest.MonkeyPatch):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
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):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
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):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
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="https://api.apimodels.app/v1",
api_key=None,
)
assert model == "gpt-5.6-luna"
assert provider == "apimodels"
assert api_key == "apimodels-env-key"
assert api_base == "https://api.apimodels.app/v1"
def test_apimodels_is_available_in_add_model_form():
fields_path = Path(litellm.__file__).parent / "proxy" / "public_endpoints" / "provider_create_fields.json"
providers = json.loads(fields_path.read_text())
apimodels = 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"] == "apimodels/claude-opus-5-5"
assert {field["key"]: field["required"] for field in apimodels["credential_fields"]} == {
"api_base": False,
"api_key": True,
}
def test_apimodels_supported_endpoints():
expected: 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,
"a2a": False,
"interactions": False,
}
backup_path = Path(litellm.__file__).parent / "provider_endpoints_support_backup.json"
root_path = Path(litellm.__file__).parent.parent / "provider_endpoints_support.json"
assert json.loads(backup_path.read_text())["providers"]["apimodels"]["endpoints"] == expected
assert json.loads(root_path.read_text())["providers"]["apimodels"]["endpoints"] == expected
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("https://api.apimodels.app/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"}],
)
request: Final = route.calls.last.request
body: Final = json.loads(request.content)
assert route.call_count == 1
assert str(request.url) == "https://api.apimodels.app/v1/chat/completions"
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("https://api.apimodels.app/v1/chat/completions").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"}]
assert response.output[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("https://api.apimodels.app/v1/chat/completions").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 response["content"][0]["text"] == "Hello from APIMODELS"
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("https://api.apimodels.app/v1/chat/completions").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"]
)