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

@ -314,5 +314,10 @@
"supports_function_calling": true,
"supports_reasoning": true
}
},
"hopbase": {
"base_url": "https://api.hop-base.com/v1",
"api_key_env": "HOPBASE_API_KEY",
"api_base_env": "HOPBASE_API_BASE"
}
}

345
litellm/model_prices_and_context_window_backup.json
View file

@ -86312,5 +86312,350 @@
"output_cost_per_token": 6e-07,
"source": "https://api.together.ai/v1/models",
"supports_prompt_caching": true
},
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},
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"mode": "chat",
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}
}

26
litellm/provider_endpoints_support_backup.json
View file

@ -2084,6 +2084,32 @@
"generateContent": true
}
},
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"display_name": "HopBase (`hopbase`)",
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"display_name": "Hosted VLLM (`hosted_vllm`)",
"url": "https://docs.litellm.ai/docs/providers/vllm",

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

@ -1987,6 +1987,34 @@
],
"default_model_placeholder": "gpt-3.5-turbo"
},
{
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"required": true,
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"default_value": null
},
{
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"label": "API Base",
"placeholder": "https://api.hop-base.com/v1",
"tooltip": "Optional. Leave blank to use the provider's default endpoint.",
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"default_value": null
}
],
"default_model_placeholder": "hopbase/glm-5.3"
},
{
"provider": "HUGGINGFACE",
"provider_display_name": "Huggingface",

1
litellm/types/utils.py
View file

@ -4731,6 +4731,7 @@ class LlmProviders(str, Enum):
MONGODB = "mongodb"
OPENSEARCH = "opensearch"
HELICONE = "helicone"
HOPBASE = "hopbase"
HYPERBOLIC = "hyperbolic"
RECRAFT = "recraft"
FAL_AI = "fal_ai"

345
model_prices_and_context_window.json
View file

@ -86312,5 +86312,350 @@
"output_cost_per_token": 6e-07,
"source": "https://api.together.ai/v1/models",
"supports_prompt_caching": 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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"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
},
"hopbase/doubao-seed-evolving": {
"cache_read_input_token_cost": 1.765e-07,
"input_cost_per_token": 8.824e-07,
"litellm_provider": "hopbase",
"max_input_tokens": 256000,
"max_output_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 4.4118e-06,
"source": "https://hop-base.com/pricing",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
},
"hopbase/gemini-3.1-pro-preview": {
"cache_read_input_token_cost": 9.12e-08,
"input_cost_per_token": 9.118e-07,
"litellm_provider": "hopbase",
"max_input_tokens": 1048576,
"max_output_tokens": 65536,
"mode": "chat",
"output_cost_per_token": 5.4706e-06,
"source": "https://hop-base.com/pricing",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
},
"hopbase/gemini-3.8-flash": {
"cache_read_input_token_cost": 3.42e-08,
"input_cost_per_token": 3.42e-07,
"litellm_provider": "hopbase",
"max_input_tokens": 1048576,
"max_output_tokens": 65536,
"mode": "chat",
"output_cost_per_token": 1.7096e-06,
"source": "https://hop-base.com/pricing",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
},
"hopbase/glm-5.3": {
"cache_read_input_token_cost": 2.21e-07,
"input_cost_per_token": 1.19e-06,
"litellm_provider": "hopbase",
"max_input_tokens": 1000000,
"max_output_tokens": 131072,
"mode": "chat",
"output_cost_per_token": 3.74e-06,
"source": "https://hop-base.com/pricing",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": false
},
"hopbase/gpt-5.6-sol": {
"cache_read_input_token_cost": 2.6e-07,
"cache_read_input_token_cost_above_272k_tokens": 5.2e-07,
"input_cost_per_token": 2.6e-06,
"input_cost_per_token_above_272k_tokens": 5.2e-06,
"litellm_provider": "hopbase",
"max_input_tokens": 922000,
"max_output_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 1.3e-05,
"output_cost_per_token_above_272k_tokens": 1.95e-05,
"source": "https://hop-base.com/pricing",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
},
"hopbase/grok-4.20-multi-agent-0309": {
"cache_read_input_token_cost": 1.2e-07,
"cache_read_input_token_cost_above_200k_tokens": 2.4e-07,
"input_cost_per_token": 7.5e-07,
"input_cost_per_token_above_200k_tokens": 1.5e-06,
"litellm_provider": "hopbase",
"max_input_tokens": 1000000,
"max_output_tokens": 30000,
"mode": "chat",
"output_cost_per_token": 1.5e-06,
"output_cost_per_token_above_200k_tokens": 3e-06,
"source": "https://hop-base.com/pricing",
"supports_function_calling": false,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": false,
"supports_tool_choice": false,
"supports_vision": true
},
"hopbase/grok-4.7": {
"cache_read_input_token_cost": 3e-07,
"cache_read_input_token_cost_above_200k_tokens": 6e-07,
"input_cost_per_token": 1.2e-06,
"input_cost_per_token_above_200k_tokens": 2.4e-06,
"litellm_provider": "hopbase",
"max_input_tokens": 500000,
"max_output_tokens": 500000,
"mode": "chat",
"output_cost_per_token": 3.6e-06,
"output_cost_per_token_above_200k_tokens": 7.2e-06,
"source": "https://hop-base.com/pricing",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
},
"hopbase/kimi-k3": {
"cache_read_input_token_cost": 1.95e-07,
"input_cost_per_token": 1.95e-06,
"litellm_provider": "hopbase",
"max_input_tokens": 1048576,
"max_output_tokens": 32768,
"mode": "chat",
"output_cost_per_token": 9.75e-06,
"source": "https://hop-base.com/pricing",
"supports_function_calling": true,
"supports_prompt_caching": true,
"supports_reasoning": true,
"supports_response_schema": true,
"supports_tool_choice": true,
"supports_vision": true
}
}

26
provider_endpoints_support.json
View file

@ -2084,6 +2084,32 @@
"generateContent": true
}
},
"hopbase": {
"display_name": "HopBase (`hopbase`)",
"url": "https://docs.litellm.ai/docs/providers/hopbase",
"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
}
},
"hosted_vllm": {
"display_name": "Hosted VLLM (`hosted_vllm`)",
"url": "https://docs.litellm.ai/docs/providers/vllm",

116
tests/unit/llms/openai_like/test_hopbase_provider.py Normal file
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@ -0,0 +1,116 @@
import json
from datetime import datetime, timezone
from typing import Final
import pytest
import respx
import litellm
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
from litellm.utils import trim_messages
def _chat_response(model: str, prompt_tokens: int, cached_tokens: int, completion_tokens: int) -> dict:
return {
"id": "chatcmpl-test",
"object": "chat.completion",
"created": 1,
"model": model,
"choices": [{"index": 0, "message": {"role": "assistant", "content": "hello"}, "finish_reason": "stop"}],
"usage": {
"prompt_tokens": prompt_tokens,
"completion_tokens": completion_tokens,
"total_tokens": prompt_tokens + completion_tokens,
"prompt_tokens_details": {"cached_tokens": cached_tokens},
},
}
def test_hopbase_resolves_environment_and_explicit_credentials(monkeypatch: pytest.MonkeyPatch) -> None:
monkeypatch.setenv("HOPBASE_API_KEY", "fake-environment-key")
assert get_llm_provider(model="hopbase/glm-5.3") == (
"glm-5.3",
"hopbase",
"fake-environment-key",
"https://api.hop-base.com/v1",
)
assert get_llm_provider(
model="hopbase/glm-5.3", api_key="fake-explicit-key", api_base="https://gateway.example/v1"
) == ("glm-5.3", "hopbase", "fake-explicit-key", "https://gateway.example/v1")
def test_hopbase_chat_sends_openai_request_and_accounts_for_cache(local_model_cost_map: None) -> None:
with respx.mock() as upstream:
route: Final = upstream.post("https://api.hop-base.com/v1/chat/completions").respond(
200, json=_chat_response("glm-5.3", prompt_tokens=100, cached_tokens=80, completion_tokens=5)
)
response: Final = litellm.completion(
model="hopbase/glm-5.3",
api_key="fake-test-key",
messages=[{"role": "user", "content": "Say hello"}],
)
request: Final = route.calls.last.request
assert request.headers["authorization"] == "Bearer fake-test-key"
assert json.loads(request.content)["model"] == "glm-5.3"
assert response.choices[0].message.content == "hello"
rates: Final = litellm.model_cost["hopbase/glm-5.3"]
expected: Final = (
20 * rates["input_cost_per_token"]
+ 80 * rates["cache_read_input_token_cost"]
+ 5 * rates["output_cost_per_token"]
)
assert litellm.completion_cost(completion_response=response) == pytest.approx(expected)
def test_hopbase_long_prompt_uses_the_upper_tier(local_model_cost_map: None) -> None:
rates: Final = litellm.model_cost["hopbase/gpt-5.6-sol"]
with respx.mock() as upstream:
upstream.post("https://api.hop-base.com/v1/chat/completions").respond(
200, json=_chat_response("gpt-5.6-sol", prompt_tokens=300_000, cached_tokens=0, completion_tokens=10)
)
response: Final = litellm.completion(
model="hopbase/gpt-5.6-sol", api_key="fake-test-key", messages=[{"role": "user", "content": "hi"}]
)
expected: Final = (
300_000 * rates["input_cost_per_token_above_272k_tokens"]
+ 10 * rates["output_cost_per_token_above_272k_tokens"]
)
assert litellm.completion_cost(completion_response=response) == pytest.approx(expected)
@pytest.mark.parametrize(
("when", "rate_source"),
[
(datetime(2026, 11, 2, 2, 0, tzinfo=timezone.utc), "peak"), # Monday 10:00 Beijing
(datetime(2026, 11, 2, 12, 0, tzinfo=timezone.utc), "off_peak"), # Monday 20:00 Beijing
(datetime(2026, 11, 1, 2, 0, tzinfo=timezone.utc), "off_peak"), # Sunday
],
)
def test_hopbase_deepseek_flash_bills_off_peak_hours(
local_model_cost_map: None, monkeypatch: pytest.MonkeyPatch, when: datetime, rate_source: str
) -> None:
from litellm.litellm_core_utils.llm_cost_calc import utils as cost_utils
monkeypatch.setattr(cost_utils, "current_billing_time", lambda: when)
info: Final = litellm.model_cost["hopbase/deepseek-v4.1-flash-1017"]
rates: Final = info if rate_source == "peak" else info["off_peak_pricing"]
prompt_cost, completion_cost = litellm.cost_per_token(
model="hopbase/deepseek-v4.1-flash-1017", prompt_tokens=1000, completion_tokens=100
)
assert prompt_cost == pytest.approx(1000 * rates["input_cost_per_token"])
assert completion_cost == pytest.approx(100 * rates["output_cost_per_token"])
def test_hopbase_models_trim_to_their_input_budget(local_model_cost_map: None) -> None:
model_names: Final = tuple(model for model in litellm.model_cost if model.startswith("hopbase/"))
assert model_names
messages: Final = [{"role": "user", "content": "hello " * 500}]
budget: Final = 100
for model in model_names:
trimmed = trim_messages(
messages,
model=model,
trim_ratio=budget / litellm.model_cost[model]["max_input_tokens"],
)
assert litellm.token_counter(model=model, messages=messages) > budget
assert litellm.token_counter(model=model, messages=trimmed) <= budget