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2
litellm/constants.py
View file

@ -966,6 +966,7 @@ openai_compatible_endpoints: Final[list] = [
"https://api.cortecs.ai/v1",
"https://api.scx.ai/v1",
"https://api.prisminference.com/v1",
"https://ai.topxea.com/v1",
"https://gigachat.devices.sberbank.ru/api/v1",
]
@ -1042,6 +1043,7 @@ openai_compatible_providers: Final[list] = [
"scx-ai",
"prism",
"sail",
"topxai",
]
OPENAI_AUDIO_TRANSCRIPTION_PROVIDERS: Final = frozenset({"openai"} | frozenset(openai_compatible_providers))

6
litellm/llms/openai_like/providers.json
View file

@ -218,5 +218,11 @@
"api_key_env": "SAIL_API_KEY",
"api_base_env": "SAIL_API_BASE",
"supported_endpoints": ["/v1/chat/completions", "/v1/responses", "/v1/messages"]
},
"topxai": {
"base_url": "https://ai.topxea.com/v1",
"api_key_env": "TOPXAI_API_KEY",
"api_base_env": "TOPXAI_API_BASE",
"supported_endpoints": ["/v1/chat/completions", "/v1/responses"]
}
}

274
litellm/model_prices_and_context_window_backup.json
View file

@ -45888,6 +45888,280 @@
"supports_tool_choice": true,
"supports_vision": true
},
"topxai/claude-sonnet-5-5": {
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"cache_creation_input_token_cost_above_1hr": 2e-06,
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"input_cost_per_token": 1e-06,
"litellm_provider": "topxai",
"max_input_tokens": 1000000,
"max_output_tokens": 128000,
"max_tokens": 128000,
"mode": "chat",
"output_cost_per_token": 5e-06,
"source": "https://ai.topxea.com/pricing/claude-sonnet-5-5",
"supports_adaptive_thinking": true,
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"supports_tool_choice": true,
"supports_vision": true
},
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"litellm_provider": "topxai",
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"mode": "chat",
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"source": "https://ai.topxea.com/pricing/claude-opus-5-5",
"supports_adaptive_thinking": true,
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},
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]
}
]
},
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},
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},
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"litellm_provider": "snowflake",
"max_input_tokens": 200000,

17
litellm/provider_endpoints_support_backup.json
View file

@ -2135,6 +2135,23 @@
"a2a": false
}
},
"topxai": {
"display_name": "TopxAI (`topxai`)",
"url": "https://docs.litellm.ai/docs/providers/topxai",
"endpoints": {
"chat_completions": true,
"messages": false,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false
}
},
"snowflake": {
"display_name": "Snowflake (`snowflake`)",
"url": "https://docs.litellm.ai/docs/providers/snowflake",

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

@ -3115,6 +3115,34 @@
],
"default_model_placeholder": "scx-ai/GLM-5.2"
},
{
"provider": "TOPXAI",
"provider_display_name": "TopxAI",
"litellm_provider": "topxai",
"credential_fields": [
{
"key": "api_base",
"label": "API Base",
"placeholder": "https://ai.topxea.com/v1",
"tooltip": null,
"required": false,
"field_type": "text",
"options": null,
"default_value": null
},
{
"key": "api_key",
"label": "API Key",
"placeholder": null,
"tooltip": null,
"required": true,
"field_type": "password",
"options": null,
"default_value": null
}
],
"default_model_placeholder": "topxai/claude-sonnet-5-5"
},
{
"provider": "Snowflake",
"provider_display_name": "Snowflake",

1
litellm/types/utils.py
View file

@ -4177,6 +4177,7 @@ class LlmProviders(str, Enum):
CORTECS = "cortecs"
SCX_AI = "scx-ai"
PRISM = "prism"
TOPXAI = "topxai"
DARKBLOOM = "darkbloom"
META = "meta"
SAIL = "sail"

274
model_prices_and_context_window.json
View file

@ -45888,6 +45888,280 @@
"supports_tool_choice": true,
"supports_vision": true
},
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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": false
},
"snowflake/claude-3-5-sonnet": {
"litellm_provider": "snowflake",
"max_input_tokens": 200000,

17
provider_endpoints_support.json
View file

@ -2419,6 +2419,23 @@
"a2a": false
}
},
"topxai": {
"display_name": "TopxAI (`topxai`)",
"url": "https://docs.litellm.ai/docs/providers/topxai",
"endpoints": {
"chat_completions": true,
"messages": false,
"responses": true,
"embeddings": false,
"image_generations": false,
"audio_transcriptions": false,
"audio_speech": false,
"moderations": false,
"batches": false,
"rerank": false,
"a2a": false
}
},
"snowflake": {
"display_name": "Snowflake (`snowflake`)",
"url": "https://docs.litellm.ai/docs/providers/snowflake",

362
tests/unit/llms/openai_like/test_topxai_provider.py Normal file
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@ -0,0 +1,362 @@
"""
Tests for the TopxAI provider configuration and integration.
"""
import json
from datetime import datetime, timezone
from typing import Final
import httpx
import pytest
import litellm
from litellm._internal_context import pinned_billing_time
from litellm.llms.custom_httpx.http_handler import HTTPHandler
# Provider endpoint: https://ai.topxea.com/docs (verified 2026-09-19)
TOPXAI_BASE_URL: Final = "https://ai.topxea.com/v1"
class TestTopxAIProviderConfig:
def test_topxai_in_provider_list(self):
from litellm import LlmProviders
assert hasattr(LlmProviders, "TOPXAI")
assert LlmProviders.TOPXAI.value == "topxai"
assert "topxai" in litellm.provider_list
def test_topxai_json_config_exists(self):
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
assert JSONProviderRegistry.exists("topxai")
topxai = JSONProviderRegistry.get("topxai")
assert topxai is not None
assert topxai.base_url == TOPXAI_BASE_URL
assert topxai.api_key_env == "TOPXAI_API_KEY"
assert topxai.api_base_env == "TOPXAI_API_BASE"
assert "/v1/responses" in topxai.supported_endpoints
def test_topxai_in_openai_compatible_providers(self):
from litellm.constants import openai_compatible_providers
assert "topxai" in openai_compatible_providers
def test_topxai_provider_resolution(self):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
model, provider, api_key, api_base = get_llm_provider(
model="topxai/claude-sonnet-5-5",
custom_llm_provider=None,
api_base=None,
api_key=None,
)
assert model == "claude-sonnet-5-5"
assert provider == "topxai"
assert api_base == TOPXAI_BASE_URL
def test_topxai_api_base_override(self):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
model, provider, api_key, api_base = get_llm_provider(
model="topxai/claude-sonnet-5-5",
custom_llm_provider=None,
api_base="https://relay.example.com/v1",
api_key="sk-test",
)
assert provider == "topxai"
assert api_base == "https://relay.example.com/v1"
assert api_key == "sk-test"
def test_topxai_url_autodetection(self):
from litellm.litellm_core_utils.get_llm_provider_logic import get_llm_provider
model, provider, api_key, api_base = get_llm_provider(
model="gpt-6.1-sol",
custom_llm_provider=None,
api_base=TOPXAI_BASE_URL,
api_key=None,
)
assert provider == "topxai"
assert api_base == TOPXAI_BASE_URL
def test_topxai_resolves_env_api_key(self, monkeypatch):
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("topxai")
assert provider is not None
config = create_config_class(provider)()
monkeypatch.setenv("TOPXAI_API_KEY", "sk-test")
api_base, api_key = config._get_openai_compatible_provider_info(None, None)
assert api_base == TOPXAI_BASE_URL
assert api_key == "sk-test"
def test_topxai_complete_url_appends_endpoint(self):
from litellm.llms.openai_like.dynamic_config import create_config_class
from litellm.llms.openai_like.json_loader import JSONProviderRegistry
provider = JSONProviderRegistry.get("topxai")
assert provider is not None
config = create_config_class(provider)()
url = config.get_complete_url(
api_base=TOPXAI_BASE_URL,
api_key="sk-test",
model="topxai/kimi-k3",
optional_params={},
litellm_params={},
stream=False,
)
assert url == f"{TOPXAI_BASE_URL}/chat/completions"
@pytest.mark.parametrize("api_base", (None, "https://relay.example.com/v1/"))
def test_topxai_responses_routes_and_transforms(
self, monkeypatch: pytest.MonkeyPatch, api_base: str | None
) -> None:
monkeypatch.setenv("TOPXAI_API_KEY", "sk-topxai-test")
expected_base: Final = (api_base or TOPXAI_BASE_URL).rstrip("/")
def respond(request: httpx.Request) -> httpx.Response:
assert request.method == "POST"
assert str(request.url) == f"{expected_base}/responses"
assert request.headers["Authorization"] == "Bearer sk-topxai-test"
payload: Final = json.loads(request.content)
assert payload["model"] == "gpt-6.1-sol"
assert payload["input"] == "Reply with OK"
return httpx.Response(
200,
json={
"id": "resp_topxai_test",
"object": "response",
"created_at": 1,
"model": "gpt-6.1-sol",
"status": "completed",
"output": [
{
"type": "message",
"id": "msg_test",
"role": "assistant",
"status": "completed",
"content": [{"type": "output_text", "text": "OK", "annotations": []}],
}
],
"usage": {"input_tokens": 4, "output_tokens": 1, "total_tokens": 5},
},
)
with httpx.Client(transport=httpx.MockTransport(respond)) as client:
response: Final = litellm.responses(
model="topxai/gpt-6.1-sol",
input="Reply with OK",
api_base=api_base,
client=HTTPHandler(client=client),
)
assert response.status == "completed"
assert response.output[0].content[0].text == "OK"
assert response.usage.input_tokens == 4
assert response.usage.output_tokens == 1
def test_topxai_router_config(self):
from litellm import Router
router = Router(
model_list=[
{
"model_name": "sonnet",
"litellm_params": {
"model": "topxai/claude-sonnet-5-5",
"api_key": "test-key",
},
}
]
)
assert len(router.model_list) == 1
assert router.model_list[0]["model_name"] == "sonnet"
class TestTopxAIModelMetadata:
# Catalog and capabilities: https://ai.topxea.com/api/pricing (verified 2026-09-30)
TOPXAI_MODELS: Final = (
"topxai/claude-sonnet-5-5",
"topxai/claude-opus-5-5",
"topxai/claude-fable-5-1",
"topxai/gpt-6.1-sol",
"topxai/gpt-6-astra",
"topxai/grok-4.7",
"topxai/kimi-k3",
"topxai/GLM-5.3-Abliterated",
"topxai/deepseek-flash",
"topxai/deepseek-v4-pro",
)
TEXT_ONLY_MODELS: Final = ("topxai/GLM-5.3-Abliterated", "topxai/deepseek-v4-pro")
# Only these routes publish a cache-write price; the rest bill cache writes as input
CACHE_WRITE_MODELS: Final = (
"topxai/claude-sonnet-5-5",
"topxai/claude-opus-5-5",
"topxai/claude-fable-5-1",
"topxai/gpt-6.1-sol",
"topxai/gpt-6-astra",
"topxai/kimi-k3",
)
# The catalogue publishes no output limit for these
NO_OUTPUT_LIMIT_MODELS: Final = ("topxai/grok-4.7",)
# First premium token and output multiplier, verified 2026-09-30:
# https://ai.topxea.com/pricing/gpt-6.1-sol
# https://ai.topxea.com/pricing/gpt-6-astra
# https://ai.topxea.com/pricing/grok-4.7
TIERED_MODELS: Final = (
("topxai/gpt-6.1-sol", 272_001, 1.5),
("topxai/gpt-6-astra", 272_001, 1.5),
("topxai/grok-4.7", 200_000, 2.0),
)
# USD for 1M prompt tokens (400K of them cache reads) and 1M completion tokens at the
# deepseek-official peak lanes, verified 2026-09-28: flash $0.24 in, $0.0048 cached, $0.96 out;
# v4-pro $1.056 in, $0.0352 cached, $3.168 out. Off-peak lanes are half.
# https://ai.topxea.com/pricing/deepseek-flash
# https://ai.topxea.com/pricing/deepseek-v4-pro
DEEPSEEK_PEAK_COSTS: Final = (
("topxai/deepseek-flash", 1.10592),
("topxai/deepseek-v4-pro", 3.81568),
)
# Peak hours are 01:00-04:00 and 06:00-10:00 UTC, Monday to Friday
DEEPSEEK_MOMENTS: Final = (
pytest.param(datetime(2026, 9, 28, 1, 0, tzinfo=timezone.utc), 1.0, id="monday-01:00"),
pytest.param(datetime(2026, 9, 28, 9, 59, tzinfo=timezone.utc), 1.0, id="monday-09:59"),
pytest.param(datetime(2026, 9, 28, 0, 59, tzinfo=timezone.utc), 0.5, id="monday-00:59"),
pytest.param(datetime(2026, 9, 28, 4, 0, tzinfo=timezone.utc), 0.5, id="monday-04:00"),
pytest.param(datetime(2026, 9, 28, 10, 0, tzinfo=timezone.utc), 0.5, id="monday-10:00"),
pytest.param(datetime(2026, 9, 27, 8, 0, tzinfo=timezone.utc), 0.5, id="sunday-08:00"),
)
@staticmethod
def _load(path_parts):
import json
from pathlib import Path
json_path = Path(__file__).parents[4].joinpath(*path_parts)
with open(json_path) as f:
return json.load(f)
def test_topxai_models_registered_with_correct_metadata(self):
model_cost = self._load(("model_prices_and_context_window.json",))
for model in self.TOPXAI_MODELS:
info = model_cost.get(model)
assert info is not None, f"{model} missing from model_prices_and_context_window.json"
assert info["litellm_provider"] == "topxai"
assert info["mode"] == "chat"
assert info["input_cost_per_token"] > 0
assert info["output_cost_per_token"] > 0
assert info["supports_function_calling"] is True
assert info["supports_tool_choice"] is True
assert info["supports_reasoning"] is True
assert info["supports_response_schema"] is True
assert info["supports_vision"] is (model not in self.TEXT_ONLY_MODELS)
assert info["supports_prompt_caching"] is True
assert 0 < info["cache_read_input_token_cost"] < info["input_cost_per_token"]
assert ("cache_creation_input_token_cost" in info) is (model in self.CACHE_WRITE_MODELS)
assert (
info.get("cache_creation_input_token_cost", info["input_cost_per_token"])
>= info["input_cost_per_token"]
)
assert ("max_output_tokens" in info) is (model not in self.NO_OUTPUT_LIMIT_MODELS)
assert info.get("max_tokens") == info.get("max_output_tokens")
assert info["max_input_tokens"] >= 500_000
assert info["source"].startswith("https://ai.topxea.com/pricing/")
@pytest.mark.parametrize("model,first_premium_token,output_multiplier", TIERED_MODELS)
@pytest.mark.parametrize("offset", (-1, 0, 1))
@pytest.mark.parametrize("cached_tokens,cache_write_tokens", ((0, 0), (1000, 0), (0, 500), (1000, 500)))
def test_topxai_cost_at_context_boundary(
self,
monkeypatch: pytest.MonkeyPatch,
model: str,
first_premium_token: int,
output_multiplier: float,
offset: int,
cached_tokens: int,
cache_write_tokens: int,
) -> None:
info: Final = self._load(("model_prices_and_context_window.json",))[model]
monkeypatch.setitem(litellm.model_cost, model, info)
prompt_tokens: Final = first_premium_token + offset
completion_tokens: Final = 17
# Whole-request input/cache rates double at these boundaries; sources above.
# Without a published cache-write price a cache write bills as input.
input_multiplier: Final = 2 if offset >= 0 else 1
expected_input: Final = input_multiplier * (
(prompt_tokens - cached_tokens - cache_write_tokens) * info["input_cost_per_token"]
+ cached_tokens * info["cache_read_input_token_cost"]
+ cache_write_tokens * info.get("cache_creation_input_token_cost", info["input_cost_per_token"])
)
expected_output: Final = (
completion_tokens * info["output_cost_per_token"] * (output_multiplier if offset >= 0 else 1)
)
prompt_cost, completion_cost = litellm.cost_per_token(
model=model,
prompt_tokens=prompt_tokens,
completion_tokens=completion_tokens,
cache_read_input_tokens=cached_tokens,
cache_creation_input_tokens=cache_write_tokens,
)
assert prompt_cost == pytest.approx(expected_input)
assert completion_cost == pytest.approx(expected_output)
@pytest.mark.parametrize("model,peak_cost", DEEPSEEK_PEAK_COSTS)
@pytest.mark.parametrize("moment,rate_share", DEEPSEEK_MOMENTS)
def test_topxai_deepseek_bills_peak_lanes_only_in_weekday_peak_hours(
self,
monkeypatch: pytest.MonkeyPatch,
model: str,
peak_cost: float,
moment: datetime,
rate_share: float,
) -> None:
monkeypatch.setitem(litellm.model_cost, model, self._load(("model_prices_and_context_window.json",))[model])
with pinned_billing_time(moment):
prompt_cost, completion_cost = litellm.cost_per_token(
model=model,
prompt_tokens=1_000_000,
completion_tokens=1_000_000,
cache_read_input_tokens=400_000,
)
assert prompt_cost + completion_cost == pytest.approx(peak_cost * rate_share)
def test_topxai_models_synced_to_backup(self):
model_cost = self._load(("model_prices_and_context_window.json",))
backup = self._load(("litellm", "model_prices_and_context_window_backup.json"))
for model in self.TOPXAI_MODELS:
assert model in backup, f"{model} missing from backup json"
assert backup[model] == model_cost[model], f"{model} differs between root and backup json"
class TestTopxAIDashboardRegistration:
@staticmethod
def _provider_create_fields():
import json
from pathlib import Path
import litellm
path = Path(litellm.__file__).parent / "proxy" / "public_endpoints" / "provider_create_fields.json"
with open(path) as f:
return json.load(f)
def test_topxai_is_selectable_in_the_add_model_form(self):
entries = [e for e in self._provider_create_fields() if e["litellm_provider"] == "topxai"]
assert len(entries) == 1, "topxai must appear exactly once in provider_create_fields.json"
entry = entries[0]
assert entry["provider"] == "TOPXAI"
assert entry["provider_display_name"] == "TopxAI"
assert entry["default_model_placeholder"].startswith("topxai/")
fields = {f["key"]: f for f in entry["credential_fields"]}
assert fields["api_key"]["required"] is True
assert fields["api_key"]["field_type"] == "password"
assert fields["api_base"]["required"] is False

1
ui/litellm-dashboard/public/assets/logos/topxai.svg Normal file
View file

@ -0,0 +1 @@
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 40 40"><rect width="40" height="40" fill="#2458ef"/><g fill="#fff" transform="translate(3 4)"><path d="M3 4H12L29 28H20L3 4Z"/><path d="M22 4H31L20 19L15.5 12.6L22 4Z"/><path d="M11.3 18.7L15.8 25.1L13.8 28H4.5L11.3 18.7Z"/></g></svg>
Side by side

After

Width:  |  Height:  |  Size: 287 B

15
ui/litellm-dashboard/src/components/provider_info_helpers.test.tsx
View file

@ -73,6 +73,17 @@ describe("provider_info_helpers", () => {
expect(fromEnumKey.logo).toBe(providerLogoMap[Providers.SCX_AI]);
});
it("should map topxai slug and TOPXAI enum key to the TopxAI display name and logo", () => {
const fromSlug = getProviderLogoAndName("topxai");
expect(fromSlug.displayName).toBe(Providers.TOPXAI);
expect(fromSlug.logo).toBe(providerLogoMap[Providers.TOPXAI]);
expect(fromSlug.logo).toBeTruthy();
const fromEnumKey = getProviderLogoAndName("TOPXAI");
expect(fromEnumKey.displayName).toBe(Providers.TOPXAI);
expect(fromEnumKey.logo).toBe(providerLogoMap[Providers.TOPXAI]);
});
it("should map bedrock_mantle slug to Bedrock Mantle display name and logo", () => {
const result = getProviderLogoAndName("bedrock_mantle");
expect(result.displayName).toBe(Providers.BedrockMantle);
@ -233,6 +244,10 @@ describe("provider_info_helpers", () => {
expect(getPlaceholder(Providers.EDENAI)).toBe("edenai/openai/gpt-mini-latest");
});
it("should return a topxai model placeholder for TOPXAI provider", () => {
expect(getPlaceholder(Providers.TOPXAI)).toBe("topxai/claude-sonnet-5-5");
});
it("should return claude-3-opus placeholder for Anthropic provider", () => {
expect(getPlaceholder(Providers.Anthropic)).toBe("claude-3-opus");
});

5
ui/litellm-dashboard/src/components/provider_info_helpers.tsx
View file

@ -57,6 +57,7 @@ import snowflakeLogo from "../../public/assets/logos/snowflake.svg";
import sonioxLogo from "../../public/assets/logos/soniox.svg";
import tencentLogo from "../../public/assets/logos/tencent.svg";
import togetheraiLogo from "../../public/assets/logos/togetherai.svg";
import topxaiLogo from "../../public/assets/logos/topxai.svg";
import topazLogo from "../../public/assets/logos/topaz.svg";
import v0Logo from "../../public/assets/logos/v0.svg";
import vercelLogo from "../../public/assets/logos/vercel.svg";
@ -169,6 +170,7 @@ export enum Providers {
Soniox = "Soniox",
TEXT_COMPLETION_CODESTRAL = "Text-Completion-Codestral",
Tencent = "Tencent",
TOPXAI = "TopxAI",
TogetherAI = "TogetherAI",
TOPAZ = "Topaz",
Triton = "Triton",
@ -289,6 +291,7 @@ export const provider_map: Record<string, string> = {
Soniox: "soniox",
TEXT_COMPLETION_CODESTRAL: "text-completion-codestral",
Tencent: "tencent",
TOPXAI: "topxai",
TogetherAI: "together_ai",
TOPAZ: "topaz",
Triton: "triton",
@ -389,6 +392,7 @@ export const providerLogoMap: Partial<Record<Providers, string>> = {
[Providers.Soniox]: sonioxLogo.src,
[Providers.Tencent]: tencentLogo.src,
[Providers.TEXT_COMPLETION_CODESTRAL]: mistralLogo.src,
[Providers.TOPXAI]: topxaiLogo.src,
[Providers.TogetherAI]: togetheraiLogo.src,
[Providers.TOPAZ]: topazLogo.src,
[Providers.Triton]: nvidiaTritonLogo.src,
@ -458,6 +462,7 @@ const providerPlaceholderMap: Partial<Record<Providers, string>> = {
[Providers.SCX_AI]: "scx-ai/GLM-5.2",
[Providers.Snowflake]: "snowflake/mistral-7b",
[Providers.Tencent]: "tencent/deepseek-v4-pro",
[Providers.TOPXAI]: "topxai/claude-sonnet-5-5",
[Providers.Vertex_AI]: "gemini-pro",
[Providers.VolcEngine]: "volcengine/<any-model-on-volcengine>",
[Providers.Voyage]: "voyage/",