Setting ‘LLM Responses ChatGPT’ Tasks

Reference example

# Fixed reference example. Playground inputs do not update this sample.
curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '[
  {
    "system_message": "communicate as if we are in a business meeting",
    "message_chain": [
      {
        "role": "user",
        "message": "Hello, what’s up?"
      },
      {
        "role": "ai",
        "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
      }
    ],
    "model_name": "gpt-4.1-mini",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]'

curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
[
  {
    "message_chain": [
      {
        "message": "Hello, what’s up?",
        "role": "user"
      },
      {
        "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?",
        "role": "ai"
      }
    ],
    "model_name": "gpt-4.1-mini",
    "system_message": "communicate as if we are in a business meeting",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]
'

import requests

url = "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post"

payload = [
    {
        "message_chain": [
            {
                "message": "Hello, what’s up?",
                "role": "user"
            },
            {
                "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?",
                "role": "ai"
            }
        ],
        "model_name": "gpt-4.1-mini",
        "system_message": "communicate as if we are in a business meeting",
        "user_prompt": "provide information on how relevant the amusement park business is in France now"
    }
]
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify([
    {
      message_chain: [
        {message: 'Hello, what’s up?', role: 'user'},
        {
          message: 'Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?',
          role: 'ai'
        }
      ],
      model_name: 'gpt-4.1-mini',
      system_message: 'communicate as if we are in a business meeting',
      user_prompt: 'provide information on how relevant the amusement park business is in France now'
    }
  ])
};

fetch('https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    [
        'message_chain' => [
                [
                                'message' => 'Hello, what’s up?',
                                'role' => 'user'
                ],
                [
                                'message' => 'Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?',
                                'role' => 'ai'
                ]
        ],
        'model_name' => 'gpt-4.1-mini',
        'system_message' => 'communicate as if we are in a business meeting',
        'user_prompt' => 'provide information on how relevant the amusement park business is in France now'
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

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

curl_close($curl);

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

package main

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

func main() {

	url := "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post"

	payload := strings.NewReader("[\n  {\n    \"message_chain\": [\n      {\n        \"message\": \"Hello, what’s up?\",\n        \"role\": \"user\"\n      },\n      {\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\",\n        \"role\": \"ai\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]")

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

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

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

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

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("[\n  {\n    \"message_chain\": [\n      {\n        \"message\": \"Hello, what’s up?\",\n        \"role\": \"user\"\n      },\n      {\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\",\n        \"role\": \"ai\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "[\n  {\n    \"message_chain\": [\n      {\n        \"message\": \"Hello, what’s up?\",\n        \"role\": \"user\"\n      },\n      {\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\",\n        \"role\": \"ai\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]"

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

{
  "cost": 123,
  "status_code": 123,
  "status_message": "<string>",
  "tasks_count": 123,
  "tasks_error": 123,
  "time": "<string>",
  "version": "<string>",
  "tasks": [
    {
      "cost": 123,
      "data": {},
      "id": "<string>",
      "path": [
        "<string>"
      ],
      "result_count": 123,
      "status_code": 123,
      "status_message": "<string>",
      "time": "<string>",
      "result": {}
    }
  ]
}

POST

https://api.aisa.one

apis

dataforseo

ai_optimization

chat_gpt

llm_responses

task_post

Reference example

# Fixed reference example. Playground inputs do not update this sample.
curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '[
  {
    "system_message": "communicate as if we are in a business meeting",
    "message_chain": [
      {
        "role": "user",
        "message": "Hello, what’s up?"
      },
      {
        "role": "ai",
        "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
      }
    ],
    "model_name": "gpt-4.1-mini",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]'

curl --request POST \
  --url https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
[
  {
    "message_chain": [
      {
        "message": "Hello, what’s up?",
        "role": "user"
      },
      {
        "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?",
        "role": "ai"
      }
    ],
    "model_name": "gpt-4.1-mini",
    "system_message": "communicate as if we are in a business meeting",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]
'

import requests

url = "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post"

payload = [
    {
        "message_chain": [
            {
                "message": "Hello, what’s up?",
                "role": "user"
            },
            {
                "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?",
                "role": "ai"
            }
        ],
        "model_name": "gpt-4.1-mini",
        "system_message": "communicate as if we are in a business meeting",
        "user_prompt": "provide information on how relevant the amusement park business is in France now"
    }
]
headers = {
    "Authorization": "Bearer <token>",
    "Content-Type": "application/json"
}

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

print(response.text)

const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
  body: JSON.stringify([
    {
      message_chain: [
        {message: 'Hello, what’s up?', role: 'user'},
        {
          message: 'Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?',
          role: 'ai'
        }
      ],
      model_name: 'gpt-4.1-mini',
      system_message: 'communicate as if we are in a business meeting',
      user_prompt: 'provide information on how relevant the amusement park business is in France now'
    }
  ])
};

fetch('https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post', options)
  .then(res => res.json())
  .then(res => console.log(res))
  .catch(err => console.error(err));

<?php

$curl = curl_init();

curl_setopt_array($curl, [
  CURLOPT_URL => "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "POST",
  CURLOPT_POSTFIELDS => json_encode([
    [
        'message_chain' => [
                [
                                'message' => 'Hello, what’s up?',
                                'role' => 'user'
                ],
                [
                                'message' => 'Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?',
                                'role' => 'ai'
                ]
        ],
        'model_name' => 'gpt-4.1-mini',
        'system_message' => 'communicate as if we are in a business meeting',
        'user_prompt' => 'provide information on how relevant the amusement park business is in France now'
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Authorization: Bearer <token>",
    "Content-Type: application/json"
  ],
]);

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

curl_close($curl);

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

package main

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

func main() {

	url := "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post"

	payload := strings.NewReader("[\n  {\n    \"message_chain\": [\n      {\n        \"message\": \"Hello, what’s up?\",\n        \"role\": \"user\"\n      },\n      {\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\",\n        \"role\": \"ai\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]")

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

	req.Header.Add("Authorization", "Bearer <token>")
	req.Header.Add("Content-Type", "application/json")

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

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

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.post("https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("[\n  {\n    \"message_chain\": [\n      {\n        \"message\": \"Hello, what’s up?\",\n        \"role\": \"user\"\n      },\n      {\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\",\n        \"role\": \"ai\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post")

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

request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "[\n  {\n    \"message_chain\": [\n      {\n        \"message\": \"Hello, what’s up?\",\n        \"role\": \"user\"\n      },\n      {\n        \"message\": \"Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?\",\n        \"role\": \"ai\"\n      }\n    ],\n    \"model_name\": \"gpt-4.1-mini\",\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"user_prompt\": \"provide information on how relevant the amusement park business is in France now\"\n  }\n]"

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

{
  "cost": 123,
  "status_code": 123,
  "status_message": "<string>",
  "tasks_count": 123,
  "tasks_error": 123,
  "time": "<string>",
  "version": "<string>",
  "tasks": [
    {
      "cost": 123,
      "data": {},
      "id": "<string>",
      "path": [
        "<string>"
      ],
      "result_count": 123,
      "status_code": 123,
      "status_message": "<string>",
      "time": "<string>",
      "result": {}
    }
  ]
}

Queues a ChatGPT prompt instead of waiting for the reply, returning a task id. Adds system_message and message_chain over the live endpoint, so a multi-turn conversation can be sent in one task. Wrapped in DataForSEO’s envelope: data in tasks[0].result, outcome in tasks[0].status_code - a rejected request still returns HTTP 200. ⚠️ Not every model supports queuing - check task_post_supported in get_dataforseo_ai_chat_gpt_llm_responses_models. Retrieve with get_dataforseo_ai_chat_gpt_llm_responses_fetch.

Example

curl -X POST "https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '[{"user_prompt": "...", "model_name": "...", "max_output_tokens": "..."}]'

Request contract

Method and path: POST /apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post
Availability: enabled
Request URL template: https://api.aisa.one/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post
Runtime facts: 6ab8fec17df1d5dcdff2e036dd1eb40587752b774ec040ab4eb0cc6946c3dd3c

Complete OpenAPI definition Effective request and pricing declaration (including authentication and referenced schemas):

{
  "operationId": "post_dataforseo_ai_chat_gpt_llm_responses_submit",
  "method": "POST",
  "path": "/apis/v1/dataforseo/ai_optimization/chat_gpt/llm_responses/task_post",
  "servers": [
    {
      "url": "https://api.aisa.one"
    }
  ],
  "parameters": [],
  "security": [
    {
      "bearerAuth": []
    }
  ],
  "requestBody": {
    "content": {
      "application/json": {
        "schema": {
          "example": [
            {
              "message_chain": [
                {
                  "message": "Hello, what’s up?",
                  "role": "user"
                },
                {
                  "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?",
                  "role": "ai"
                }
              ],
              "model_name": "gpt-4.1-mini",
              "system_message": "communicate as if we are in a business meeting",
              "user_prompt": "provide information on how relevant the amusement park business is in France now"
            }
          ],
          "items": {
            "oneOf": [
              {
                "example": [
                  {
                    "message_chain": [
                      {
                        "message": "Hello, what’s up?",
                        "role": "user"
                      },
                      {
                        "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?",
                        "role": "ai"
                      }
                    ],
                    "model_name": "gpt-4.1-mini",
                    "system_message": "communicate as if we are in a business meeting",
                    "user_prompt": "provide information on how relevant the amusement park business is in France now"
                  }
                ],
                "properties": {
                  "force_web_search": {
                    "description": "<em>force AI agent to use web search</em><br>optional field<br>to enable this parameter, <code>web_search</code> must also be enabled;<br>when enabled, the AI model is forced to access and cite current web information;<br>default value: <code>false</code>;<br><strong>Note:</strong> even if the parameter is set to <code>true</code>, there is no guarantee web sources will be cited in the response <br><strong>Note #2:</strong> not supported in reasoning models",
                    "type": [
                      "boolean",
                      "null"
                    ]
                  },
                  "max_output_tokens": {
                    "description": "<em>maximum number of tokens in the AI response</em><br>optional field<br>minimum value for reasoning models (e.g., <code>reasoning</code> is <code>true</code> in the <a href=\"/v3/ai_optimization/chat_gpt/llm_responses/models/\" target=\"_blank\">Models endpoint</a>): <code>1024</code>;<br>minimum value for non-reasoning models: <code>16</code>;<br>maximum value: <code>4096</code>;<br>default value: <code>2048</code>",
                    "type": [
                      "integer",
                      "null"
                    ]
                  },
                  "message_chain": {
                    "description": "conversation history\noptional field\narray of message objects representing previous conversation turns;\neach object must contain:\nrole string with either user or ai role;\nmessage string with message content (max 500 characters);\nyou can specify maximum of 10 message objects in the array;\nNote: for Perplexity models, messages must strictly alternate between user and AI roles (user → ai);\nexample:\n\"message_chain\": [{\"role\":\"user\",\"message\":\"Hello, what’s up?\"},{\"role\":\"ai\",\"message\":\"Hello! I’m doing well, thank you. How can I assist you today?\"}]",
                    "items": {
                      "oneOf": [
                        {
                          "properties": {
                            "message": {
                              "description": "message text",
                              "type": [
                                "string",
                                "null"
                              ]
                            },
                            "role": {
                              "description": "role of the user from whom the message originates",
                              "type": [
                                "string",
                                "null"
                              ]
                            }
                          },
                          "type": "object"
                        }
                      ],
                      "type": [
                        "object",
                        "null"
                      ]
                    },
                    "type": [
                      "array",
                      "null"
                    ]
                  },
                  "model_name": {
                    "description": "<em>name of the AI model</em><br><strong>required field</strong><br><code>model_name</code >consists of the actual model name and version name;<br>if the basic model name is specified, its latest version will be set by default;<br>for example, if <code>gpt-4.1</code> is specified, the <code>gpt-4.1-2025-04-14</code> will be set as <code>model_name</code> automatically;<br>you can receive the list of available LLM models by making a separate request to the <code>https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/models</code>",
                    "type": "string"
                  },
                  "pingback_url": {
                    "description": "<em>notification URL of a completed task</em><br>optional field<br>when a task is completed we will notify you by GET request sent to the URL you have specified<br>you can use the ‘$id’ string as a <code>$id</code> variable and ‘$tag’ as urlencoded <code>$tag</code> variable. We will set the necessary values before sending the request<br>example:<br><code>http://your-server.com/pingscript?id=$id</code><br><code>http://your-server.com/pingscript?id=$id&amp;tag=$tag</code><br><strong>Note:</strong> special character in <code>pingback_url</code> will be urlencoded;<br>i.a., the <code>#</code> character will be encoded into <code>%23</code><p>learn more on our <a href=\"https://dataforseo.com/help-center/pingbacks-postbacks-with-dataforseo-api\" target=\"_blank\" rel=\"noopener noreferrer\">Help Center</a>",
                    "type": [
                      "string",
                      "null"
                    ]
                  },
                  "postback_url": {
                    "description": "<em>URL for sending task results</em><br>optional field<br>once the task is completed, we will send a POST request with its results compressed in the <code>gzip</code> format to the <code>postback_url</code> you specified<br>you can use the ‘$id’ string as a <code>$id</code> variable and ‘$tag’ as urlencoded <code>$tag</code> variable. We will set the necessary values before sending the request.<br>example:<br><code>http://your-server.com/postbackscript?id=$id</code><br><code>http://your-server.com/postbackscript?id=$id&amp;tag=$tag</code><br><strong>Note:</strong> special character in <code>postback_url</code> will be urlencoded;<br>i.a., the <code>#</code> character will be encoded into <code>%23</code><p>learn more on our <a href=\"https://dataforseo.com/help-center/pingbacks-postbacks-with-dataforseo-api\" target=\"_blank\" rel=\"noopener noreferrer\">Help Center</a>",
                    "type": [
                      "string",
                      "null"
                    ]
                  },
                  "system_message": {
                    "description": "<em>instructions for the AI behaviour</em><br>optional field<br>defines the AI's role, tone, or specific behavior;<br>you can specify <strong>up to 500 characters</strong> in the <code>system_message</code> field",
                    "type": [
                      "string",
                      "null"
                    ]
                  },
                  "tag": {
                    "description": "<em>user-defined task identifier</em><br>optional field<br><em>the character limit is 255</em><br>you can use this parameter to identify the task and match it with the result<br>you will find the specified <code>tag</code> value in the <code>data</code> array of the response",
                    "type": [
                      "string",
                      "null"
                    ]
                  },
                  "temperature": {
                    "description": "<em>randomness of the AI response</em><br>optional field<br>higher values make output more diverse; <br>lower values make output more focused;<br>minimum value: <code>0</code><br>maximum value: <code>2</code><br>default value: <code>0.94</code><br><strong>Note:</strong> not supported in reasoning models",
                    "type": [
                      "number",
                      "null"
                    ]
                  },
                  "top_p": {
                    "description": "<em>diversity of the AI response</em><br>optional field <br>controls diversity of the response by limiting token selection;<br>minimum value: <code>0</code><br>maximum value: <code>1</code> <br>default value: <code>0.92</code><p><strong>Note:</strong>  <code>top_p</code> cannot be used together with <code>temperature</code> in the same request",
                    "type": [
                      "number",
                      "null"
                    ]
                  },
                  "user_prompt": {
                    "description": "<em>prompt for the AI model</em><br><strong>required field</strong><br>the question or task you want to send to the AI model;<br>you can specify <strong>up to 500 characters</strong> in the <code>user_prompt</code> field",
                    "type": "string"
                  },
                  "web_search": {
                    "description": "<em>enable web search</em><br>optional field<br>when enabled, the AI model can access and cite current web information;<br>default value: <code>false</code>;<br><strong>Note:</strong> refer to the <a href=\"https://docs.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/models/\">Models endpoint</a> for a list of models that support <code>web_search</code>;",
                    "type": [
                      "boolean",
                      "null"
                    ]
                  },
                  "web_search_city": {
                    "description": "<em>city name of the location</em><br>optional field<br><strong>Note:</strong> not supported in <code>o3-mini</code>, <code>o1-pro</code>, <code>o1</code> models",
                    "type": [
                      "string",
                      "null"
                    ]
                  },
                  "web_search_country_iso_code": {
                    "description": "<em>ISO country code of the location</em><br>optional field<br>to enable this parameter, <code>web_search</code> must also be enabled;<br>when enabled, the AI model will search the web from the country you specify;<br><strong>Note:</strong> not supported in <code>o3-mini</code>, <code>o1-pro</code>, <code>o1</code> models",
                    "type": [
                      "string",
                      "null"
                    ]
                  }
                },
                "type": "object"
              }
            ],
            "type": [
              "object",
              "null"
            ]
          },
          "type": "array"
        }
      }
    }
  },
  "x-aisa-status": "enabled",
  "x-aisa-pricing": {
    "cost_contract": "response_usd",
    "customer_multiplier": 1.0,
    "customer_pricing": "provider_cost_multiplier",
    "default_request_estimate_usd": 0.701,
    "estimate_source": "profile_seed",
    "evidence_source": "response_body"
  },
  "components": {
    "securitySchemes": {
      "bearerAuth": {
        "scheme": "bearer",
        "type": "http"
      }
    }
  }
}

Cost and request rules

Usage-based billing; Default request estimate: $0.701 USD (not a fixed price). Account-specific pricing and final settlement may differ. Published reference values are not an account-specific quote or a settled charge. For endpoints supporting X-AISA-Cost-Mode: quote, obtain a free quote for your exact request before execution; reconcile the settled cost with response metadata and Usage Logs. USD amounts use 1,000,000 integer micro-USD per dollar.

Default request reference estimate (USD): 0.701
Cost contract: response_usd
Published reference multiplier: 1.0
Settlement evidence source: response_body

Authorizations

Authorization

string

header

required

Bearer authentication header of the form Bearer <token>, where <token> is your auth token.

Body

application/json

force_web_search

boolean | null

force AI agent to use web search
optional field
to enable this parameter, web_search must also be enabled;
when enabled, the AI model is forced to access and cite current web information;
default value: false;
Note: even if the parameter is set to true, there is no guarantee web sources will be cited in the response
Note #2: not supported in reasoning models

max_output_tokens

integer | null

maximum number of tokens in the AI response
optional field
minimum value for reasoning models (e.g., reasoning is true in the Models endpoint): 1024;
minimum value for non-reasoning models: 16;
maximum value: 4096;
default value: 2048

message_chain

(object | null)[] | null

conversation history optional field array of message objects representing previous conversation turns; each object must contain: role string with either user or ai role; message string with message content (max 500 characters); you can specify maximum of 10 message objects in the array; Note: for Perplexity models, messages must strictly alternate between user and AI roles (user → ai); example: "message_chain": [{"role":"user","message":"Hello, what’s up?"},{"role":"ai","message":"Hello! I’m doing well, thank you. How can I assist you today?"}]

Show child attributes

model_name

string

name of the AI model
required field
model_nameconsists of the actual model name and version name;
if the basic model name is specified, its latest version will be set by default;
for example, if gpt-4.1 is specified, the gpt-4.1-2025-04-14 will be set as model_name automatically;
you can receive the list of available LLM models by making a separate request to the https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/models

pingback_url

string | null

notification URL of a completed task
optional field
when a task is completed we will notify you by GET request sent to the URL you have specified
you can use the ‘$id’ string as a $id variable and ‘$tag’ as urlencoded $tag variable. We will set the necessary values before sending the request
example:
http://your-server.com/pingscript?id=$id
http://your-server.com/pingscript?id=$id&tag=$tag
Note: special character in pingback_url will be urlencoded;
i.a., the # character will be encoded into %23

learn more on our Help Center

postback_url

string | null

URL for sending task results
optional field
once the task is completed, we will send a POST request with its results compressed in the gzip format to the postback_url you specified
you can use the ‘$id’ string as a $id variable and ‘$tag’ as urlencoded $tag variable. We will set the necessary values before sending the request.
example:
http://your-server.com/postbackscript?id=$id
http://your-server.com/postbackscript?id=$id&tag=$tag
Note: special character in postback_url will be urlencoded;
i.a., the # character will be encoded into %23

learn more on our Help Center

system_message

string | null

instructions for the AI behaviour
optional field
defines the AI's role, tone, or specific behavior;
you can specify up to 500 characters in the system_message field

tag

string | null

user-defined task identifier
optional field
the character limit is 255
you can use this parameter to identify the task and match it with the result
you will find the specified tag value in the data array of the response

temperature

number | null

randomness of the AI response
optional field
higher values make output more diverse;
lower values make output more focused;
minimum value: 0
maximum value: 2
default value: 0.94
Note: not supported in reasoning models

top_p

number | null

diversity of the AI response
optional field
controls diversity of the response by limiting token selection;
minimum value: 0
maximum value: 1
default value: 0.92

Note: top_p cannot be used together with temperature in the same request

user_prompt

string

prompt for the AI model
required field
the question or task you want to send to the AI model;
you can specify up to 500 characters in the user_prompt field

web_search

boolean | null

enable web search
optional field
when enabled, the AI model can access and cite current web information;
default value: false;
Note: refer to the Models endpoint for a list of models that support web_search;

web_search_city

string | null

city name of the location
optional field
Note: not supported in o3-mini, o1-pro, o1 models

web_search_country_iso_code

string | null

ISO country code of the location
optional field
to enable this parameter, web_search must also be enabled;
when enabled, the AI model will search the web from the country you specify;
Note: not supported in o3-mini, o1-pro, o1 models

Example:

[
  {
    "message_chain": [
      {
        "message": "Hello, what’s up?",
        "role": "user"
      },
      {
        "message": "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?",
        "role": "ai"
      }
    ],
    "model_name": "gpt-4.1-mini",
    "system_message": "communicate as if we are in a business meeting",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]

Response

object | null

Successful operation

cost

number<double> | null

total tasks cost, USD

status_code

integer | null

general status code you can find the full list of the response codes here

status_message

string | null

general informational message you can find the full list of general informational messages here

tasks_count

integer<int64> | null

the number of tasks in the tasks array

tasks_error

integer<int64> | null

the number of tasks in the tasks array returned with an error

time

string | null

total execution time, seconds

version

string | null

the current version of the API

tasks

(object | null)[] | null

array of tasks

Show child attributes

Overview

Models

Search & Research

Finance & Crypto

CoinGecko

Duffel Flights

DataForSEO

Social & Media

SEO & Web Intel

Sales & Email

Travel & Other

Setting ‘LLM Responses ChatGPT’ Tasks

Example

Request contract

Cost and request rules

Authorizations

Body

Response

​Example

​Request contract

​Cost and request rules

Authorizations

Body

Response

Example

Request contract

Cost and request rules