ChatGPT LLM Responses endpoint allows you to retrieve structured responses from a specific ChatGPT model, based on the input parameters.
This is the Standard method of data retrieval. If you don’t need to receive data in real-time, this method is the best option for you. Set a task and retrieve the results when our system collects them. Execution time depends on the system workload.
If your system requires delivering instant results, the Live method will be a better solution. This method doesn’t require making separate POST and GET requests to the corresponding endpoints.
Note that this endpoint requires making an automatic prepayment of $0.01 to execute the task. If the cost charged by the LLM is less than $0.01, the difference will be refunded to your account balance.
Instead of ‘login’ and ‘password’ use your credentials from https://app.dataforseo.com/api-access
# Instead of 'login' and 'password' use your credentials from https://app.dataforseo.com/api-access
login="login"
password="password"
cred="$(printf ${login}:${password} | base64)"
curl --location --request POST "https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post" \
--header "Authorization: Basic ${cred}" \
--header "Content-Type: application/json" \
--data-raw '[
{
"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"
}
]'
<?php
/**
* Method: POST
* Endpoint: https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post
* @see https://docs.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post
*/
require_once __DIR__ . '/../../../../../lib/RestClient.php';
$config = require __DIR__ . '/../../../../../lib/config.php';
$client = new RestClient($config['base_url'], null, $config['login'], $config['password']);
$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',
],
];
try {
$result = $client->post('/v3/ai_optimization/chat_gpt/llm_responses/task_post', $data);
print_r($result);
// do something with post result
} catch (RestClientException $e) {
printf(
"HTTP code: %dnError code: %dnMessage: %snTrace: %sn",
$e->getHttpCode(),
$e->getCode(),
$e->getMessage(),
$e->getTraceAsString()
);
}
?>
"""
Method: POST
Endpoint: https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post
@see https://docs.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post
"""
import sys
import os
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../../')))
from lib.client import RestClient
from lib.config import username, password
client = RestClient(username, password)
post_data = []
post_data.append({
'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'
})
try:
response = client.post('/v3/ai_optimization/chat_gpt/llm_responses/task_post', post_data)
print(response)
# do something with post result
except Exception as e:
print(f'An error occurred: {e}')
const post_array = [];
post_array.push({
"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"
});
const axios = require('axios');
axios({
method: 'post',
url: 'https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post',
auth: {
username: 'login',
password: 'password'
},
data: post_array,
headers: {
'content-type': 'application/json'
}
}).then(function (response) {
var result = response['data']['tasks'];
// Result data
console.log(result);
}).catch(function (error) {
console.log(error);
});
using System;
using System.Linq;
using System.Net.Http;
using System.Net.Http.Headers;
using System.Text;
using System.Collections.Generic;
using System.Threading.Tasks;
using Newtonsoft.Json;
namespace DataForSeoSdk;
public class AiOptimization
{
private static readonly HttpClient _httpClient;
static AiOptimization()
{
_httpClient = new HttpClient
{
BaseAddress = new Uri("https://api.dataforseo.com/")
};
_httpClient.DefaultRequestHeaders.Authorization =
new AuthenticationHeaderValue("Basic", ApiConfig.Base64Auth);
}
/// <summary>
/// Method: POST
/// Endpoint: https://api.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post
/// </summary>
/// <see href="https://docs.dataforseo.com/v3/ai_optimization/chat_gpt/llm_responses/task_post"/>
public static async Task ChatGPTLlmResponsesTaskPost()
{
var postData = new List<object>();
// a simple way to set a task, the full list of possible parameters is available in documentation
postData.Add(new
{
system_message = "communicate as if we are in a business meeting",
message_chain = new object[]
{
new
{
role = "user",
message = "Hello, what's up?"
},
new
{
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 = 1024,
temperature = 0.3,
top_p = 0.5,
model_name = "gpt-4.1-mini",
user_prompt = "provide information on how relevant the amusement park business is in France now"
});
var content = new StringContent(JsonConvert.SerializeObject(postData), Encoding.UTF8, "application/json");
using var response = await _httpClient.PostAsync("/v3/ai_optimization/chat_gpt/llm_responses/task_post", content);
var result = JsonConvert.DeserializeObject<dynamic>(await response.Content.ReadAsStringAsync());
// you can find the full list of the response codes here https://docs.dataforseo.com/v3/appendix/errors
if (result.status_code == 20000)
{
// do something with result
Console.WriteLine(result);
}
else
Console.WriteLine($"error. Code: {result.status_code} Message: {result.status_message}");
}
The above command returns JSON structured like this:
{
"version": "0.1.20250526",
"status_code": 20000,
"status_message": "Ok.",
"time": "0.1071 sec.",
"cost": 0.0102,
"tasks_count": 1,
"tasks_error": 0,
"tasks": [
{
"id": "07151610-0696-0613-0000-b2366402ce99",
"status_code": 20100,
"status_message": "Task Created.",
"time": "0.0146 sec.",
"cost": 0.0102,
"result_count": 0,
"path": [
"v3",
"ai_optimization",
"chat_gpt",
"llm_responses",
"task_post"
],
"data": {
"api": "ai_optimization",
"function": "llm_responses",
"se": "chat_gpt",
"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"
},
"result": null
}
]
}
All POST data should be sent in the JSON format (UTF-8 encoding). Task setting is done using the POST method. When setting a task, you should send all task parameters in the task array of the generic POST array. You can send up to 2000 API calls per minute, with each POST call containing no more than 100 tasks. If your POST call contains over 100 tasks, the tasks over this limit will return the 40006 error.
Tasks using the Standard method may take up to 72 hours to complete. If the task is not completed within this time, it is marked as failed, and the $0.01 advance is refunded. It is also important to note that if your account balance is negative, you will not receive the results even if the task is completed successfully.
You can also retrieve the results of completed tasks using the unique task identifier id. Alternatively, we can send them to you as soon as they are ready if you specify the postback_url or pingback_url when setting a task. Note that if your server doesn’t respond within 10 seconds, the connection will be aborted by timeout, and the task will be transferred to the tasks_ready list. The error code and message depend on your server’s configuration.
See Help Center to learn more about using pingbacks and postbacks with DataForSEO APIs.
Below you will find a detailed description of the fields you can use for setting a task.
Description of the fields for setting a task:
Field name
Type
Description
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
maximum number of tokens in the AI response
optional field
minimum value for reasoning models (e.g., model_name starts with ‘o’): 1024;
minimum value for non-reasoning models: 16;
maximum value for reasoning models: 4096;
maximum value for non-reasoning models: 2048;
default value for both reasoning and non-reasoning models: 2048;
temperature
float
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 o4-mini, o3-mini, o1-pro, o1 models
top_p
float
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: not supported in o4-mini, o3-mini, o1-pro, o1 models
system_message
string
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
array
conversation history
optional field
array of message objects representing previous conversation turns;
each object must contain role and message parameters: role string with either user or ai role; message string with message content (max 500 characters);
you can specify the maximum of 10 message objects in the array;
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?"}]
tag
string
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
postback_url
string
return 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
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
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
As a response of the API server, you will receive JSON-encoded data containing a tasks array with the information specific to the set tasks.
Description of the fields in the results array:
Field name
Type
Description
version
string
the current version of the API
status_code
integer
general status code
you can find the full list of the response codes here Note: we strongly recommend designing a necessary system for handling related exceptional or error conditions
status_message
string
general informational message
you can find the full list of general informational messages here
time
string
execution time, seconds
cost
float
total tasks cost, USD
tasks_count
integer
the number of tasks in the tasksarray
tasks_error
integer
the number of tasks in the tasks array returned with an error
tasks
array
array of tasks
id
string
unique task identifier in our system unique task identifier in the UUID format
status_code
integer
status code of the task
generated by DataForSEO; can be within the following range: 10000-60000
you can find the full list of response codes here
status_message
string
informational message of the task
you can find the full list of general informational messages here
time
string
execution time, seconds
cost
float
cost of the task, USD
result_count
integer
number of elements in the result array
path
array
URL path
data
object
contains the same parameters that you specified in the POST request
result
array
array of results
in this case, the value will be null