Live ChatGPT LLM Responses

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/live \
  --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?"
      }
    ],
    "max_output_tokens": 200,
    "temperature": 0.3,
    "top_p": 0.5,
    "model_name": "gpt-4.1-mini",
    "web_search": true,
    "web_search_country_iso_code": "FR",
    "web_search_city": "Paris",
    "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/live \
  --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?"
      }
    ],
    "max_output_tokens": 200,
    "temperature": 0.3,
    "top_p": 0.5,
    "model_name": "gpt-4.1-mini",
    "web_search": true,
    "web_search_country_iso_code": "FR",
    "web_search_city": "Paris",
    "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/live"

payload = [
    {
        "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?"
            }
        ],
        "max_output_tokens": 200,
        "temperature": 0.3,
        "top_p": 0.5,
        "model_name": "gpt-4.1-mini",
        "web_search": True,
        "web_search_country_iso_code": "FR",
        "web_search_city": "Paris",
        "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([
    {
      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?'
        }
      ],
      max_output_tokens: 200,
      temperature: 0.3,
      top_p: 0.5,
      model_name: 'gpt-4.1-mini',
      web_search: true,
      web_search_country_iso_code: 'FR',
      web_search_city: 'Paris',
      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/live', 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/live",
  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([
    [
        '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?'
                ]
        ],
        'max_output_tokens' => 200,
        'temperature' => 0.3,
        'top_p' => 0.5,
        'model_name' => 'gpt-4.1-mini',
        'web_search' => true,
        'web_search_country_iso_code' => 'FR',
        'web_search_city' => 'Paris',
        '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/live"

	payload := strings.NewReader("[\n  {\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\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      }\n    ],\n    \"max_output_tokens\": 200,\n    \"temperature\": 0.3,\n    \"top_p\": 0.5,\n    \"model_name\": \"gpt-4.1-mini\",\n    \"web_search\": true,\n    \"web_search_country_iso_code\": \"FR\",\n    \"web_search_city\": \"Paris\",\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/live")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("[\n  {\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\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      }\n    ],\n    \"max_output_tokens\": 200,\n    \"temperature\": 0.3,\n    \"top_p\": 0.5,\n    \"model_name\": \"gpt-4.1-mini\",\n    \"web_search\": true,\n    \"web_search_country_iso_code\": \"FR\",\n    \"web_search_city\": \"Paris\",\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/live")

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    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\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      }\n    ],\n    \"max_output_tokens\": 200,\n    \"temperature\": 0.3,\n    \"top_p\": 0.5,\n    \"model_name\": \"gpt-4.1-mini\",\n    \"web_search\": true,\n    \"web_search_country_iso_code\": \"FR\",\n    \"web_search_city\": \"Paris\",\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

{
  "version": "<string>",
  "status_code": 123,
  "status_message": "<string>",
  "time": "<string>",
  "cost": 123,
  "tasks_count": 123,
  "tasks_error": 123,
  "tasks": [
    {
      "id": "<string>",
      "status_code": 123,
      "status_message": "<string>",
      "time": "<string>",
      "cost": 123,
      "result_count": 123,
      "path": [
        "<string>"
      ],
      "data": {},
      "result": [
        {
          "model_name": "<string>",
          "input_tokens": 123,
          "output_tokens": 123,
          "reasoning_tokens": 123,
          "web_search": true,
          "money_spent": 123,
          "datetime": "<string>",
          "items": [
            {
              "sections": [
                {
                  "type": "<string>",
                  "text": "<string>"
                }
              ],
              "type": "<string>"
            }
          ],
          "fan_out_queries": [
            "<string>"
          ]
        }
      ]
    }
  ]
}

POST

https://api.aisa.one

apis

dataforseo

ai_optimization

chat_gpt

llm_responses

live

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/live \
  --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?"
      }
    ],
    "max_output_tokens": 200,
    "temperature": 0.3,
    "top_p": 0.5,
    "model_name": "gpt-4.1-mini",
    "web_search": true,
    "web_search_country_iso_code": "FR",
    "web_search_city": "Paris",
    "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/live \
  --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?"
      }
    ],
    "max_output_tokens": 200,
    "temperature": 0.3,
    "top_p": 0.5,
    "model_name": "gpt-4.1-mini",
    "web_search": true,
    "web_search_country_iso_code": "FR",
    "web_search_city": "Paris",
    "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/live"

payload = [
    {
        "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?"
            }
        ],
        "max_output_tokens": 200,
        "temperature": 0.3,
        "top_p": 0.5,
        "model_name": "gpt-4.1-mini",
        "web_search": True,
        "web_search_country_iso_code": "FR",
        "web_search_city": "Paris",
        "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([
    {
      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?'
        }
      ],
      max_output_tokens: 200,
      temperature: 0.3,
      top_p: 0.5,
      model_name: 'gpt-4.1-mini',
      web_search: true,
      web_search_country_iso_code: 'FR',
      web_search_city: 'Paris',
      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/live', 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/live",
  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([
    [
        '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?'
                ]
        ],
        'max_output_tokens' => 200,
        'temperature' => 0.3,
        'top_p' => 0.5,
        'model_name' => 'gpt-4.1-mini',
        'web_search' => true,
        'web_search_country_iso_code' => 'FR',
        'web_search_city' => 'Paris',
        '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/live"

	payload := strings.NewReader("[\n  {\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\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      }\n    ],\n    \"max_output_tokens\": 200,\n    \"temperature\": 0.3,\n    \"top_p\": 0.5,\n    \"model_name\": \"gpt-4.1-mini\",\n    \"web_search\": true,\n    \"web_search_country_iso_code\": \"FR\",\n    \"web_search_city\": \"Paris\",\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/live")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("[\n  {\n    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\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      }\n    ],\n    \"max_output_tokens\": 200,\n    \"temperature\": 0.3,\n    \"top_p\": 0.5,\n    \"model_name\": \"gpt-4.1-mini\",\n    \"web_search\": true,\n    \"web_search_country_iso_code\": \"FR\",\n    \"web_search_city\": \"Paris\",\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/live")

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    \"system_message\": \"communicate as if we are in a business meeting\",\n    \"message_chain\": [\n      {\n        \"role\": \"user\",\n        \"message\": \"Hello, what’s up?\"\n      },\n      {\n        \"role\": \"ai\",\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      }\n    ],\n    \"max_output_tokens\": 200,\n    \"temperature\": 0.3,\n    \"top_p\": 0.5,\n    \"model_name\": \"gpt-4.1-mini\",\n    \"web_search\": true,\n    \"web_search_country_iso_code\": \"FR\",\n    \"web_search_city\": \"Paris\",\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

{
  "version": "<string>",
  "status_code": 123,
  "status_message": "<string>",
  "time": "<string>",
  "cost": 123,
  "tasks_count": 123,
  "tasks_error": 123,
  "tasks": [
    {
      "id": "<string>",
      "status_code": 123,
      "status_message": "<string>",
      "time": "<string>",
      "cost": 123,
      "result_count": 123,
      "path": [
        "<string>"
      ],
      "data": {},
      "result": [
        {
          "model_name": "<string>",
          "input_tokens": 123,
          "output_tokens": 123,
          "reasoning_tokens": 123,
          "web_search": true,
          "money_spent": 123,
          "datetime": "<string>",
          "items": [
            {
              "sections": [
                {
                  "type": "<string>",
                  "text": "<string>"
                }
              ],
              "type": "<string>"
            }
          ],
          "fan_out_queries": [
            "<string>"
          ]
        }
      ]
    }
  ]
}

Sends a user_prompt to a ChatGPT model and returns its reply, synchronously. model_name picks the model - see get_dataforseo_ai_chat_gpt_llm_responses_models, and an unlisted name is rejected outright. max_output_tokens, temperature and top_p behave as they do on the vendor’s own API, and web_search or force_web_search let it browse. Returns model_name, input_tokens, output_tokens, reasoning_tokens, web_search, money_spent, datetime, items and fan_out_queries. 💰 Billed per call at a flat rate, whatever the reply length. Wrapped in DataForSEO’s envelope: data in tasks[0].result, outcome in tasks[0].status_code - a rejected request still returns HTTP 200. ⚠️ This is a general LLM call routed through a data provider, not an SEO measurement. If you want to know what ChatGPT tells users about a brand, post_dataforseo_ai_chat_gpt_llm_scraper_live reproduces the consumer experience instead.

Example

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

// Copy this whole block into Claude Code, Codex, Cursor or any coding agent.
// It carries everything needed to connect, authorize and run this example —
// no key to paste, one click of Allow in the browser.
{
  "operation_id": "post_dataforseo_ai_chat_gpt_llm_responses_live",
  "arguments": {},
  "max_price_usd": 0.05
}
// ── for the agent ─────────────────────────────────────────────────────────
// Register the AIsa MCP server, then call its `use` tool with the JSON above.
//   name       aisa
//   endpoint   https://mcp.aisa.one/mcp
//   transport  streamable-http
//   auth       OAuth. The server publishes RFC 9728 metadata, so open a
//              browser and the user clicks Allow once — nothing to paste.
//              If AISA_API_KEY is set, send it as a bearer header instead.
// Use your own MCP mechanism, not a hand-edited config file:
//   Codex        codex mcp add aisa --url https://mcp.aisa.one/mcp
//   Claude Code  claude mcp add --transport http -s user aisa https://mcp.aisa.one/mcp
//   Cursor / VS Code   a "url" entry for https://mcp.aisa.one/mcp in the MCP config
// The server lists five tools over https://mcp.aisa.one/servers worth of APIs:
// search, get_details, use, batch_use, list_categories. search and
// get_details are free; use is billed per call and max_price_usd refuses
// anything above the cap before spending. This operation's full contract —
// every argument, the response shape, the price and the pitfalls — is at
//   https://aisa.one/docs/api-reference/dataforseo/post_dataforseo-ai-optimization-chat-gpt-llm-responses-live.md
// Then run the call and show me the result.

First time? Point any MCP client at https://mcp.aisa.one/mcp — Claude Code, Codex, Cursor, VS Code and the rest. Authorization is OAuth: the client opens a browser, you click Allow once, and there is no key to paste. The commands per client, and what each call costs, are on aisa.one/mcp.

Set this endpoint up in your agent →

Cost and request rules

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): 0.0695
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

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

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

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
Note: if web_search is set to true or the reasoning model is specified in the request, the output token count may exceed the specified max_output_tokens limit

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

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;

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

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

web_search_city

string | null

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

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

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

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 object of the response

Example:

[
  {
    "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?"
      }
    ],
    "max_output_tokens": 200,
    "temperature": 0.3,
    "top_p": 0.5,
    "model_name": "gpt-4.1-mini",
    "web_search": true,
    "web_search_country_iso_code": "FR",
    "web_search_city": "Paris",
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]

Response

object | null

Successful operation

version

string | null

the current version of the API

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

time

string | null

total execution time, seconds

cost

number<double> | null

total tasks cost, USD

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

tasks

(object | null)[] | null

array of tasks

Show child attributes

​Example

​Cost and request rules

Authorizations

Body

Response

Example

Cost and request rules