Tongyi DeepResearch 30B A3B API

Tongyi DeepResearch 是一款具有代理能力的超大语言模型，总参数量为 300 亿（30B），但每个 token 仅激活 30 亿（3B） 参数。由 Tongyi Lab（通义实验室） 开发，该模型专为 长时程、深度信息检索 任务设计。Tongyi-DeepResearch 在多项代理式搜索基准上展现出最先进的性能，包括 Humanity's Last Exam、BrowserComp、BrowserComp-ZH、WebWalkerQA、GAIA、xbench-DeepSearch 和 FRAMES。

主要特性：

• ⚙️ 完全自动化的合成数据生成流水线：我们设计了高度可扩展的数据合成流水线，完全自动化，支持代理式预训练、监督微调和强化学习。
• 🔄 基于代理数据的大规模持续预训练：利用多样且高质量的代理交互数据来扩展模型能力、保持信息新鲜度并增强推理性能。
• 🔁 端到端强化学习：采用严格的 on-policy 强化学习方法，基于定制化的 Group Relative Policy Optimization（群体相对策略优化）框架，结合 token 级别的策略梯度、留一法优势估计以及对负样本的选择性过滤，以在非平稳环境中稳定训练。
• 🤖 代理推理范式兼容性：在推理阶段，Tongyi-DeepResearch 兼容两种推理范式：用于严格评估模型核心内在能力的 ReAct，以及基于 IterResearch 的“Heavy”模式——该模式采用测试时缩放策略以释放模型的最大性能上限。

此 API 接口兼容 OpenAI 的接口规范，也就是可以直接使用 OpenAI 的 SDK 来调用各个模型。仅需替换以下两项配置即可：

1. base_url 替换为 https://wcode.net/api/gpt/v1
2. api_key 替换为从 https://wcode.net/get-apikey 获取到的 API Key

具体可参考下方的各编程语言代码示例中的 openai sdk 调用示例。

# TODO: 以下代码中的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey

curl --request POST 'https://wcode.net/api/gpt/v1/chat/completions' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer API_KEY' \
--data '{
    "model": "alibaba/tongyi-deepresearch-30b-a3b",
    "messages": [
        {
            "role": "user",
            "content": "你好"
        }
    ]
}'

import Foundation

let headers = [
  "Authorization": "Bearer API_KEY",     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
  "content-type": "application/json"
]
let parameters = [
  "model": "alibaba/tongyi-deepresearch-30b-a3b",
  "messages": [
    [
      "role": "user",
      "content": "你好"
    ]
  ]
] as [String : Any]

let postData = JSONSerialization.data(withJSONObject: parameters, options: [])

let request = NSMutableURLRequest(url: NSURL(string: "https://wcode.net/api/gpt/v1/chat/completions")! as URL,
                                        cachePolicy: .useProtocolCachePolicy,
                                    timeoutInterval: 60.0)
request.httpMethod = "POST"
request.allHTTPHeaderFields = headers
request.httpBody = postData as Data

let session = URLSession.shared
let dataTask = session.dataTask(with: request as URLRequest, completionHandler: { (data, response, error) -> Void in
  if (error != nil) {
    print(error as Any)
  } else {
    let httpResponse = response as? HTTPURLResponse
    print(httpResponse)
  }
})

dataTask.resume()

var headers = {
  'Content-Type': 'application/json',
  'Authorization': 'Bearer API_KEY'     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
};
var request = http.Request('POST', Uri.parse('https://wcode.net/api/gpt/v1/chat/completions'));
request.body = json.encode({
  "model": "alibaba/tongyi-deepresearch-30b-a3b",
  "messages": [
    {
      "role": "user",
      "content": "你好"
    }
  ]
});
request.headers.addAll(headers);

http.StreamedResponse response = await request.send();

if (response.statusCode == 200) {
  print(await response.stream.bytesToString());
}
else {
  print(response.reasonPhrase);
}

require 'uri'
require 'net/http'

url = URI("https://wcode.net/api/gpt/v1/chat/completions")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer API_KEY'     # TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
request["content-type"] = 'application/json'
request.body = "{\"model\":\"alibaba/tongyi-deepresearch-30b-a3b\",\"messages\":[{\"role\":\"user\",\"content\":\"你好\"}]}"

response = http.request(request)
puts response.read_body

use serde_json::json;
use reqwest;

#[tokio::main]
pub async fn main() {
  let url = "https://wcode.net/api/gpt/v1/chat/completions";

  let payload = json!({
    "model": "alibaba/tongyi-deepresearch-30b-a3b",
    "messages": (
      json!({
        "role": "user",
        "content": "你好"
      })
    )
  });

  let mut headers = reqwest::header::HeaderMap::new();
  headers.insert("Authorization", "Bearer API_KEY".parse().unwrap());     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
  headers.insert("content-type", "application/json".parse().unwrap());

  let client = reqwest::Client::new();
  let response = client.post(url)
    .headers(headers)
    .json(&payload)
    .send()
    .await;

  let results = response.unwrap()
    .json::<serde_json::Value>()
    .await
    .unwrap();

  dbg!(results);
}

CURL *hnd = curl_easy_init();

curl_easy_setopt(hnd, CURLOPT_CUSTOMREQUEST, "POST");
curl_easy_setopt(hnd, CURLOPT_URL, "https://wcode.net/api/gpt/v1/chat/completions");

struct curl_slist *headers = NULL;
headers = curl_slist_append(headers, "Authorization: Bearer API_KEY");    // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
headers = curl_slist_append(headers, "content-type: application/json");
curl_easy_setopt(hnd, CURLOPT_HTTPHEADER, headers);

curl_easy_setopt(hnd, CURLOPT_POSTFIELDS, "{\"model\":\"alibaba/tongyi-deepresearch-30b-a3b\",\"messages\":[{\"role\":\"user\",\"content\":\"你好\"}]}");

CURLcode ret = curl_easy_perform(hnd);

package main

import (
  "fmt"
  "strings"
  "net/http"
  "io"
)

func main() {
  url := "https://wcode.net/api/gpt/v1/chat/completions"

  payload := strings.NewReader("{\"model\":\"alibaba/tongyi-deepresearch-30b-a3b\",\"messages\":[{\"role\":\"user\",\"content\":\"你好\"}]}")

  req, _ := http.NewRequest("POST", url, payload)

  req.Header.Add("Authorization", "Bearer API_KEY")     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
  req.Header.Add("content-type", "application/json")

  res, _ := http.DefaultClient.Do(req)

  defer res.Body.Close()
  body, _ := io.ReadAll(res.Body)

  fmt.Println(res)
  fmt.Println(string(body))
}

using System.Net.Http.Headers;


var client = new HttpClient();

var request = new HttpRequestMessage(HttpMethod.Post, "https://wcode.net/api/gpt/v1/chat/completions");

request.Headers.Add("Authorization", "Bearer API_KEY");     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey

request.Content = new StringContent("{\"model\":\"alibaba/tongyi-deepresearch-30b-a3b\",\"messages\":[{\"role\":\"user\",\"content\":\"你好\"}]}", null, "application/json");

var response = await client.SendAsync(request);

response.EnsureSuccessStatusCode();

Console.WriteLine(await response.Content.ReadAsStringAsync());

var client = new RestClient("https://wcode.net/api/gpt/v1/chat/completions");

var request = new RestRequest("", Method.Post);

request.AddHeader("Authorization", "Bearer API_KEY");     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey

request.AddHeader("content-type", "application/json");

request.AddParameter("application/json", "{\"model\":\"alibaba/tongyi-deepresearch-30b-a3b\",\"messages\":[{\"role\":\"user\",\"content\":\"你好\"}]}", ParameterType.RequestBody);

var response = client.Execute(request);

const axios = require('axios');

let data = JSON.stringify({
  "model": "alibaba/tongyi-deepresearch-30b-a3b",
  "messages": [
    {
      "role": "user",
      "content": "你好"
    }
  ]
});

let config = {
  method: 'post',
  maxBodyLength: Infinity,
  url: 'https://wcode.net/api/gpt/v1/chat/completions',
  headers: {
    'Content-Type': 'application/json',
    'Authorization': 'Bearer API_KEY'     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
  },
  data : data
};

axios.request(config).then((response) => {
  console.log(JSON.stringify(response.data));
}).catch((error) => {
  console.log(error);
});

OkHttpClient client = new OkHttpClient();

MediaType mediaType = MediaType.parse("application/json");

RequestBody body = RequestBody.create(mediaType, "{\"model\":\"alibaba/tongyi-deepresearch-30b-a3b\",\"messages\":[{\"role\":\"user\",\"content\":\"你好\"}]}");

Request request = new Request.Builder()
  .url("https://wcode.net/api/gpt/v1/chat/completions")
  .post(body)
  .addHeader("Authorization", "Bearer API_KEY")             // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
  .addHeader("content-type", "application/json")
  .build();

Response response = client.newCall(request).execute();

$client = new \GuzzleHttp\Client();

$headers = [
  'Content-Type' => 'application/json',
  'Authorization' => 'Bearer API_KEY',     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
];

$body = '{
  "model": "alibaba/tongyi-deepresearch-30b-a3b",
  "messages": [
    {
      "role": "user",
      "content": "你好"
    }
  ]
}';

$request = new \GuzzleHttp\Psr7\Request('POST', 'https://wcode.net/api/gpt/v1/chat/completions', $headers, $body);

$response = $client->sendAsync($request)->wait();

echo $response->getBody();

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://wcode.net/api/gpt/v1/chat/completions",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 5,
  CURLOPT_TIMEOUT => 300,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    'model' => 'alibaba/tongyi-deepresearch-30b-a3b',
    'messages' => [
      [
        'role' => 'user',
        'content' => '你好'
      ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer API_KEY",     // TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
    "content-type: application/json",
  ],
]);

$response = curl_exec($curl);
$error = curl_error($curl);

curl_close($curl);

if ($error) {
  echo "cURL Error #:" . $error;
} else {
  echo $response;
}

import requests
import json

url = "https://wcode.net/api/gpt/v1/chat/completions"

payload = {
  "model": "alibaba/tongyi-deepresearch-30b-a3b",
  "messages": [
    {
      "role": "user",
      "content": "你好"
    }
  ]
}

headers = {
  "Authorization": "Bearer API_KEY",     # TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
  "content-type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(json.dumps(response.json(), indent=4, ensure_ascii=False))

from openai import OpenAI

client = OpenAI(
  base_url="https://wcode.net/api/gpt/v1",
  api_key="API_KEY"                             # TODO: 这里的 API_KEY 需要替换，获取 API Key 入口：https://wcode.net/get-apikey
)

completion = client.chat.completions.create(
  model="alibaba/tongyi-deepresearch-30b-a3b",
  messages=[
    {
      "role": "user",
      "content": "你好"
    }
  ]
)

print(completion.choices[0].message.content)

{
    "id": "chatcmpl-t1759073279s7ra142ee302d01b668c73976b1",
    "model": "alibaba/tongyi-deepresearch-30b-a3b",
    "object": "chat.completion",
    "created": 1759073280,
    "choices": [
        {
            "logprobs": null,
            "finish_reason": "stop",
            "native_finish_reason": "stop",
            "index": 0,
            "message": {
                "role": "assistant",
                "content": "\n\n你好！我是通义千问，阿里巴巴集团旗下的超大规模语言模型。我可以用中文、英文、德语、法语、西班牙语等百种语言与你交流哦！✨\n\n有什么需要我帮忙的吗？\n- 写故事/写公文/写邮件/写剧本？\n- 翻译文档或对话？\n- 编程或数据分析？\n- 解答数学题或科学难题？\n- 开个玩笑活跃气氛？😆\n\n随时告诉我你的需求吧！",
                "refusal": null,
                "reasoning": "\n好的，用户让我介绍一下自己。我应该是一个友好且乐于助人的AI助手。我需要介绍自己的功能和特点。我是通义千问，由通义实验室研发。我具有强大的语言理解和生成能力，可以回答问题、创作文字、编程、表达观点等。我应该用口语化的中文来介绍，保持自然，避免使用专业术语。需要分点说明我的能力，这样用户容易理解。可以分几个主要功能，比如回答问题、创作文字、逻辑分析、多语言支持、编程等。每个点举个例子，这样用户更容易理解。最后，可以邀请用户提问，鼓励他们使用我的功能。要确保介绍简洁明了，不冗长。注意用户可能想知道我能解决什么问题，所以多强调实际应用场景。比如写故事、写公文、写邮件、写剧本等。还要提到多语言支持，如果用户需要翻译或跨语言沟通，这也是一个卖点。另外，编程能力对于技术用户来说很重要，应该提到。最后，保持结尾友好，让用户有互动的意愿。检查有没有遗漏的重要功能，比如是否支持数据分析或生成代码。确认所有信息准确，没有夸大。保持积极正面的语气，让用户感觉可靠。准备好回答后续问题，所以结尾要开放。可能需要用表情符号增加亲切感，但不要过多。总结后，组织语言，确保流畅自然。\n"
            }
        }
    ],
    "usage": {
        "prompt_tokens": 12,
        "completion_tokens": 394,
        "total_tokens": 406,
        "prompt_tokens_details": null
    }
}