문서

빠른 시작

PingToken은 여러 AI 모델에 액세스할 수 있는 통합 API 엔드포인트를 제공합니다. 모든 요청은 다음 URL로 전송됩니다: https://pingtoken.cn/api/v1

curl https://pingtoken.cn/api/v1/chat/completions \
  -H "Authorization: Bearer YOUR_API_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "deepseek-v4-pro",
    "messages": [{"role": "user", "content": "Hello"}]
  }'

Base URL: https://pingtoken.cn/api/v1

인증

콘솔의 토큰 관리 페이지에서 API 토큰을 생성하세요. 각 토큰은 특정 모델 및 속도 제한으로 제한할 수 있습니다. 모든 API 요청의 Authorization 헤더에 토큰을 포함해야 합니다.

Include your token in the Authorization header as a Bearer token:

Authorization: Bearer YOUR_API_TOKEN

Tokens are created from the Console → Token Management page. The raw token value is shown only once upon creation — store it securely.

채팅 완성

AI 모델에 메시지를 보내고 응답을 받습니다. 스트리밍 및 비스트리밍 모드를 모두 지원합니다.

Endpoint

POST https://pingtoken.cn/api/v1/chat/completions

Request Body

Response

{
  "id": "chatcmpl-abc123",
  "model": "deepseek-v4-pro",
  "choices": [{
    "index": 0,
    "message": {
      "role": "assistant",
      "content": "Hello! How can I help you today?"
    },
    "finish_reason": "stop"
  }],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 9,
    "total_tokens": 19
  }
}

Code Examples

import requests

url = "https://pingtoken.cn/api/v1/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_TOKEN",
    "Content-Type": "application/json"
}
data = {
    "model": "deepseek-v4-pro",
    "messages": [
        {"role": "user", "content": "Hello, how are you?"}
    ]
}

response = requests.post(url, headers=headers, json=data)
result = response.json()

# Print the assistant's reply
print(result["choices"][0]["message"]["content"])
print(f"Tokens used: {result['usage']['total_tokens']}")

# Streaming example
data["stream"] = True
with requests.post(url, headers=headers, json=data, stream=True) as r:
    for line in r.iter_lines():
        if line:
            line = line.decode("utf-8")
            if line.startswith("data: ") and line != "data: [DONE]":
                import json
                chunk = json.loads(line[6:])
                content = chunk["choices"][0].get("delta", {}).get("content", "")
                if content:
                    print(content, end="", flush=True)

import java.net.URI;
import java.net.http.*;
import java.io.*;
import com.google.gson.*;

public class ChatExample {
    public static void main(String[] args) throws Exception {
        HttpClient client = HttpClient.newHttpClient();

        JsonObject data = new JsonObject();
        data.addProperty("model", "deepseek-v4-pro");
        JsonArray messages = new JsonArray();
        JsonObject msg = new JsonObject();
        msg.addProperty("role", "user");
        msg.addProperty("content", "Hello, how are you?");
        messages.add(msg);
        data.add("messages", messages);

        HttpRequest request = HttpRequest.newBuilder()
            .uri(URI.create("https://pingtoken.cn/api/v1/chat/completions"))
            .header("Authorization", "Bearer YOUR_API_TOKEN")
            .header("Content-Type", "application/json")
            .POST(HttpRequest.BodyPublishers.ofString(data.toString()))
            .build();

        HttpResponse<String> response = client.send(request,
            HttpResponse.BodyHandlers.ofString());

        JsonObject result = JsonParser.parseString(response.body())
            .getAsJsonObject();

        String content = result.getAsJsonArray("choices")
            .get(0).getAsJsonObject()
            .getAsJsonObject("message")
            .get("content").getAsString();
        System.out.println(content);

        int totalTokens = result.getAsJsonObject("usage")
            .get("total_tokens").getAsInt();
        System.out.println("Tokens used: " + totalTokens);
    }
}

// Requires: JDK 11+ (HttpClient), Gson for JSON parsing
// Add to pom.xml:
// <dependency>
//   <groupId>com.google.code.gson</groupId>
//   <artifactId>gson</artifactId>
//   <version>2.10.1</version>
// </dependency>

// Node.js 18+ (built-in fetch)
// For older Node.js, install: npm install node-fetch

const url = "https://pingtoken.cn/api/v1/chat/completions";
const headers = {
  "Authorization": "Bearer YOUR_API_TOKEN",
  "Content-Type": "application/json"
};
const body = {
  model: "deepseek-v4-pro",
  messages: [
    { role: "user", content: "Hello, how are you?" }
  ]
};

// Non-streaming request
const response = await fetch(url, {
  method: "POST",
  headers,
  body: JSON.stringify(body)
});
const result = await response.json();

console.log(result.choices[0].message.content);
console.log(`Tokens used: ${result.usage.total_tokens}`);

// Streaming request
body.stream = true;
const streamResp = await fetch(url, {
  method: "POST",
  headers,
  body: JSON.stringify(body)
});

const reader = streamResp.body.getReader();
const decoder = new TextDecoder();
let buffer = "";

while (true) {
  const { done, value } = await reader.read();
  if (done) break;
  buffer += decoder.decode(value, { stream: true });
  const lines = buffer.split("\n");
  buffer = lines.pop() || "";
  for (const line of lines) {
    if (line.startsWith("data: ") && line !== "data: [DONE]") {
      const chunk = JSON.parse(line.slice(6));
      const content = chunk.choices[0]?.delta?.content;
      if (content) process.stdout.write(content);
    }
  }
}

사용 가능한 모델

모델 광장에서 모든 지원 모델과 상세 가격을 확인하세요.

Note: PingToken only supports domestic Chinese AI models. Foreign models (Claude, OpenAI/GPT, Gemini, etc.) are not supported.

Popular models include:

View the complete list at the Model Plaza.

가격

각 모델은 1,000 토큰당 가격이 책정되며 입력과 출력에 다른 요금이 적용됩니다. Pro 사용자는 할인된 요금이 적용됩니다. 구체적인 가격은 모델 광장을 확인하세요.

Usage costs are deducted from your wallet balance automatically. You can top up your balance from the Console → Wallet page.

속도 제한

무료 사용자: 분당 60회 요청. Pro 사용자: 분당 600회 요청. 더 높은 제한이 필요하시면 문의하세요.

Rate limits are applied per API token. When the limit is exceeded, the API returns a 429 Too Many Requests response. Each token can have its own rate limit configured during creation.

클라이언트 설정

Configure your PingToken model in various client tools. Replace MODEL_ID with the actual model ID and YOUR_API_TOKEN with your PingToken API key.

Base URL: https://pingtoken.cn/api/v1

Cursor

File: ~/.cursor/settings.json

{
  "chat.model": "MODEL_ID",
  "chat.openAiApiOverride": "https://pingtoken.cn/api/v1",
  "chat.openAiApiKeyOverride": "YOUR_API_TOKEN"
}

Cherry Studio

在 Cherry Studio 的模型提供商配置中添加 OpenAI 兼容提供商：

{
  "type": "openai",
  "name": "PingToken",
  "apiKey": "YOUR_API_TOKEN",
  "apiUrl": "https://pingtoken.cn/api/v1",
  "models": ["MODEL_ID"]
}

Cline (VS Code Extension)

File: cline_mcp_settings.json

{
  "apiProvider": "openai",
  "openAiApiKey": "YOUR_API_TOKEN",
  "openAiBaseUrl": "https://pingtoken.cn/api/v1",
  "openAiModelId": "MODEL_ID"
}

Dify

在 Dify 的模型提供商中选择「OpenAI API Compatible」：

{
  "provider": "openai_api_compatible",
  "model": "MODEL_ID",
  "api_key": "YOUR_API_TOKEN",
  "base_url": "https://pingtoken.cn/api/v1"
}

Claude Code Router

File: ~/.claude-code-router/config.json

{
  "Providers": [
    {
      "name": "pingtoken",
      "api_base_url": "https://pingtoken.cn/api/v1/chat/completions",
      "api_key": "YOUR_API_TOKEN",
      "models": ["MODEL_ID"]
    }
  ],
  "Router": {
    "default": "pingtoken,MODEL_ID"
  }
}

Codex CLI

通过环境变量或配置文件设置：

# 环境变量方式
export CODEX_API_BASE="https://pingtoken.cn/api/v1"
export CODEX_MODEL="MODEL_ID"
export CODEX_API_KEY="YOUR_API_TOKEN"

# codex_config.json 方式:
# {
#   "apiBase": "https://pingtoken.cn/api/v1",
#   "model": "MODEL_ID",
#   "apiKey": "YOUR_API_TOKEN"
# }

Qwen Code

File: qwen_code_config.json

{
  "api_base": "https://pingtoken.cn/api/v1",
  "api_key": "YOUR_API_TOKEN",
  "model": "MODEL_ID"
}

Lingma

File: lingma_config.json

{
  "apiKey": "YOUR_API_TOKEN",
  "baseUrl": "https://pingtoken.cn/api/v1",
  "model": "MODEL_ID"
}

도움말 문서

欢迎使用 PingToken 帮助文档。这里提供详细的使用指南和操作说明，帮助您快速上手平台功能。

Authorization: Bearer YOUR_API_TOKEN

import requests

url = "https://pingtoken.cn/api/v1/chat/completions"
headers = {
    "Authorization": "Bearer YOUR_API_TOKEN",
    "Content-Type": "application/json"
}
data = {
    "model": "deepseek-v4-pro",
    "messages": [{"role": "user", "content": "Hello"}]
}

response = requests.post(url, headers=headers, json=data)
print(response.json()["choices"][0]["message"]["content"])