API 文件

一個 OpenAI 相容端點,用 DeepSeek、Qwen、GLM。改 base_url 就好,程式不用改。

快速開始

Python(openai SDK)

pip install openai

from openai import OpenAI

client = OpenAI(
    base_url="https://vantixcloud.com/v1",
    api_key="vx-your-key",
)

r = client.chat.completions.create(
    model="deepseek-flash",
    messages=[{"role": "user", "content": "Hello"}],
)
print(r.choices[0].message.content)

Node.js(openai SDK)

npm install openai · ES modules(頂層 await)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://vantixcloud.com/v1",
  apiKey: process.env.VANTIX_KEY, // vx-...
});

const r = await client.chat.completions.create({
  model: "deepseek-flash",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

curl

curl https://vantixcloud.com/v1/chat/completions \
  -H "Authorization: Bearer $VANTIX_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-flash","messages":[{"role":"user","content":"Hello"}]}'

串流

設 stream: true 就會收到標準 OpenAI 格式的 SSE。閘道會向上游要求串流的 token 用量(stream_options.include_usage),所以最後一個 chunk 帶有 usage,餘額就是依此扣款。

Python

stream = client.chat.completions.create(
    model="deepseek-flash",
    messages=[{"role": "user", "content": "Write a haiku about latency"}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)
    if chunk.usage:  # last chunk carries token usage
        print("\n", chunk.usage)

Node.js

const stream = await client.chat.completions.create({
  model: "deepseek-flash",
  messages: [{ role: "user", content: "Write a haiku about latency" }],
  stream: true,
});
for await (const chunk of stream) {
  const t = chunk.choices?.[0]?.delta?.content;
  if (t) process.stdout.write(t);
  if (chunk.usage) console.log("\n", chunk.usage); // last chunk
}

curl

curl -N https://vantixcloud.com/v1/chat/completions \
  -H "Authorization: Bearer $VANTIX_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"deepseek-flash","stream":true,"messages":[{"role":"user","content":"Hello"}]}'

模型

模型 ID 區分大小寫。以下是目前端點上的模型;GET /v1/models 回傳同一份清單。

curl https://vantixcloud.com/v1/models

Kimi、Seedance 2.5、Wan,以及 Claude、GPT、Gemini 為報價制供應,不在這個端點的固定清單上。想用原廠牌價比較每月費用,請用大模型 API 費用計算機。

查餘額與用量

把 key POST 到 /api/keys,帶 action: "balance",會回傳餘額、累計花費與呼叫次數(美元)。非串流的回應也會帶兩個標頭:

串流回應不帶這兩個標頭,請用查餘額。

curl -X POST https://vantixcloud.com/api/keys \
  -H "Content-Type: application/json" \
  -d '{"action":"balance","key":"vx-your-key"}'

# {"ok":true,"email":"...","balance":12.3456,"spent":7.6544,"calls":1234,"created":"..."}

錯誤碼

錯誤採用 OpenAI 的錯誤格式,SDK 會照常拋出例外:

{"error": {"message": "Insufficient balance. Top up at ...", "type": "invalid_request_error"}}

頻率限制

閘道本身不設每把 key 的頻率限制。上游供應商有各自的限制;它們回 429 時我們原樣轉回。請用指數退避重試:

import time, openai

def call_with_retry(fn, tries=5):
    for i in range(tries):
        try:
            return fn()
        except openai.RateLimitError:          # HTTP 429 passed through from upstream
            time.sleep(min(2 ** i, 30))
        except openai.APIStatusError as e:     # retry 502/503 only
            if e.status_code not in (502, 503):
                raise
            time.sleep(min(2 ** i, 30))
    raise RuntimeError("still failing, contact Telegram @sapporo168")

接入各種工具

只要支援「OpenAI 相容」供應商的工具都能用:填 base URL、貼上 key、選模型 ID。

Cline / Roo Code (VS Code)

• API Provider:OpenAI Compatible
• Base URL:https://vantixcloud.com/v1
• API Key:vx-…
• Model ID:deepseek-v4-pro 或 deepseek-flash

LibreChat

# librechat.yaml
endpoints:
  custom:
    - name: "VantixCloud"
      apiKey: "${VANTIX_KEY}"
      baseURL: "https://vantixcloud.com/v1"
      models:
        default: ["deepseek-flash", "deepseek-v4-pro", "qwen3.7-plus", "glm-5.3"]
        fetch: true
      titleConvo: true
      modelDisplayLabel: "VantixCloud"

Cherry Studio / ChatBox / NextChat

• 供應商類型:OpenAI
• API 位址:https://vantixcloud.com(有些 App 要不含 /v1 的位址;失敗就改成加 /v1)
• API Key:vx-…
• 手動新增模型:deepseek-flash、deepseek-v4-pro、qwen3.8-max、glm-5.3

one-api / new-api

• 渠道類型:OpenAI
• Base URL:https://vantixcloud.com
• Key:vx-…
• 模型:貼上方的 ID

LangChain

from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model="deepseek-flash", base_url="https://vantixcloud.com/v1", api_key="vx-...")

疑難排解