The cost of the task can be calculated on the Pricing page.
Live Gemini LLM Responses
Live Gemini LLM Responses endpoint allows you to retrieve structured responses from a specific Gemini AI model, based on the input parameters.
Live Gemini LLM Responses endpoint allows you to retrieve structured responses from a specific Gemini AI model, based on the input parameters.
The cost of the task can be calculated on the Pricing page.
All POST data should be sent in the JSON format (UTF-8 encoding). The 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, each Live Gemini LLM Responses call can contain only one task.
Execution time for tasks set with the Live Gemini LLM Responses endpoint is currently up to 120 seconds.
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_promptfield | 
| 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 gemini-1.5-prois specified, thegemini-1.5-pro-002will be set asmodel_nameautomatically;you can receive the list of available LLM models by making a separate request to the https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/models | 
| max_output_tokens | integer | maximum number of tokens in the AI response optional field minimum value: 1maximum value: 2048default value: 2048Note: when web_searchis set totrue, the output token count may exceed the specifiedmax_output_tokenslimit | 
| temperature | float | randomness of the AI response optional field higher values make output more diverse lower values make output more focused minimum value: 0maximum value: 2default value: 1.3 | 
| top_p | float | diversity of the AI response optional field controls diversity of the response by limiting token selection minimum value: 0maximum value: 1default value: 0.9 | 
| web_search | boolean | enable web search for current information optional field when enabled, the AI model can access and cite current web information; Note: refer to the Models endpoint for a list of models that support web_search;default value: false; | 
| system_message | string | instructions for the AI behavior optional field defines the AI’s role, tone, or specific behavior you can specify up to 500 characters in the system_messagefield | 
| message_chain | array | conversation history optional field array of message objects representing previous conversation turns; each object must contain roleandmessageparameters:rolestring with eitheruserorairole;messagestring 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 tagvalue in thedataobject 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 tasksarray returned with an error | 
| tasks | array | array of tasks | 
| id | string | task identifier unique task identifier in our system 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 the 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 includes the base task price plus the money_spentvalue | 
| result_count | integer | number of elements in the resultarray | 
| path | array | URL path | 
| data | object | contains the same parameters that you specified in the POST request | 
| result | array | array of results | 
| model_name | string | name of the AI model used | 
| input_tokens | integer | number of tokens in the input total count of tokens processed | 
| output_tokens | integer | number of tokens in the output total count of tokens generated in the AI response | 
| web_search | boolean | indicates if web search was used | 
| money_spent | float | cost of AI tokens, USD the price charged by the third-party AI model provider for according to its Pricing | 
| datetime | string | date and time when the result was received in the UTC format: “yyyy-mm-dd hh-mm-ss +00:00” example: 2019-11-15 12:57:46 +00:00 | 
| items | array | array of response items contains structured AI response data | 
| type | string | type of the element = ‘message’ | 
| sections | array | array of content sections contains different parts of the AI response | 
| type | string | type of element = ‘text’ | 
| text | string | AI-generated text content | 
| annotations | array | array of references used to generate the response equals nullif theweb_searchparameter is not set totrueNote: annotationsmay return empty even whenweb_searchistrue, as the AI will attempt to retrieve web information but may not find relevant results | 
| title | string | the domain name or title of the quoted source | 
| url | string | redirect URL to the quoted source contains a Vertex AI redirect that leads to the original source | 
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/gemini/llm_responses/live" 
--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?"
      }
    ],
    "max_output_tokens": 200,
    "temperature": 0.3,
    "top_p": 0.5,
    "model_name": "gemini-2.5-flash",
    "web_search": true,
    "user_prompt": "provide information on how relevant the amusement park business is in France now"
  }
]"<?php
// You can download this file from here https://cdn.dataforseo.com/v3/examples/php/php_RestClient.zip
require('RestClient.php');
$api_url = 'https://api.dataforseo.com/';
try {
   // Instead of 'login' and 'password' use your credentials from https://app.dataforseo.com/api-access
   $client = new RestClient($api_url, null, 'login', 'password');
} catch (RestClientException $e) {
   echo "n";
   print "HTTP code: {$e->getHttpCode()}n";
   print "Error code: {$e->getCode()}n";
   print "Message: {$e->getMessage()}n";
   print  $e->getTraceAsString();
   echo "n";
   exit();
}
$post_array = array();
// You can set only one task at a time
$post_array[] = array(
        "system_message" => "communicate as if we are in a business meeting",
        "message_chain" => [
            [
                "role"    => "user",
                "message" => "Hello, what's up?"
            ],
            [
                "role"    => "ai",
                "message" => "Hello! I’m doing well, thank you. How can I assist you today? Are there any specific topics or projects you’d like to discuss in our meeting?"
            ]
        ],
        "max_output_tokens" => 200,
        "temperature" => 0.3,
        "top_p" => 0.5,
        "model_name" => "gemini-2.5-flash",
        "web_search" => true,
        "user_prompt" => "provide information on how relevant the amusement park business is in France now"
);
if (count($post_array) > 0) {
try {
    // POST /v3/ai_optimization/gemini/llm_responses/live
    // in addition to 'google' and 'ai_mode' you can also set other search engine and type parameters
    // the full list of possible parameters is available in documentation
    $result = $client->post('/v3/ai_optimization/gemini/llm_responses/live', $post_array);
    print_r($result);
    // do something with post result
} catch (RestClientException $e) {
    echo "n";
    print "HTTP code: {$e->getHttpCode()}n";
    print "Error code: {$e->getCode()}n";
    print "Message: {$e->getMessage()}n";
    print  $e->getTraceAsString();
    echo "n";
}
$client = null;
?>const axios = require('axios');
axios({
    method: 'post',
    url: 'https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live',
    auth: {
        username: 'login',
        password: 'password'
    },
    data: [{
    system_message: encodeURI("communicate as if we are in a business meeting"),
    message_chain: [
      {
        role: "user",
        message: "Hello, what’s up?"
      },
      {
        role: "ai",
        message: encodeURI("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: "gemini-2.5-flash",
    web_search: true,
    user_prompt: encodeURI("provide information on how relevant the amusement park business is in France now")
    }],
    headers: {
        'content-type': 'application/json'
    }
}).then(function (response) {
    var result = response['data']['tasks'];
    // Result data
    console.log(result);
}).catch(function (error) {
    console.log(error);
});"""
Method: POST
Endpoint: https://api.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live
@see https://docs.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live
"""
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?'
            }
        ],
        'max_output_tokens': 200,
        'temperature': 0.3,
        'top_p': 0.5,
        'model_name': 'gemini-2.5-flash',
        'web_search': True,
        'user_prompt': 'provide information on how relevant the amusement park business is in France now'
    })
try:
    response = client.post('/v3/ai_optimization/gemini/llm_responses/live', post_data)
    print(response)
    # do something with post result
except Exception as e:
    print(f'An error occurred: {e}')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/gemini/llm_responses/live
    /// </summary>
    /// <see href="https://docs.dataforseo.com/v3/ai_optimization/gemini/llm_responses/live"/>
    
    public static async Task GeminiLlmResponsesLive()
    {
        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 = 200,
            temperature = 0.3,
            top_p = 0.5,
            model_name = "gemini-2.5-flash",
            web_search = true,
            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/gemini/llm_responses/live", 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": "14.6761 sec.",
  "cost": 0.0357548,
  "tasks_count": 1,
  "tasks_error": 0,
  "tasks": [
    {
      "id": "07021706-1535-0612-0000-e8acb319f072",
      "status_code": 20000,
      "status_message": "Ok.",
      "time": "14.3327 sec.",
      "cost": 0.0357548,
      "result_count": 1,
      "path": [
        "v3",
        "ai_optimization",
        "gemini",
        "llm_responses",
        "live"
      ],
      "data": {
        "api": "ai_optimization",
        "function": "llm_responses",
        "se": "gemini",
        "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": "gemini-2.5-flash",
        "user_prompt": "provide information on how relevant the amusement park business is in France now"
      },
      "result": [
        {
          "model_name": "gemini-2.5-flash",
          "input_tokens": 68,
          "output_tokens": 241,
          "web_search": true,
          "money_spent": 0.0351548,
          "datetime": "2025-07-02 14:06:32 +00:00",
          "items": [
            {
              "type": "message",
              "sections": [
                {
                  "type": "text",
                  "text": "The amusement park business in France is highly relevant and a significant part of the country's tourism and leisure industry. Here's a breakdown of its current relevance:nn**1. Market Size and Growth:**n*   The French amusement parks market generated a revenue of USD 3,249.3 million in 2024.n*   It is projected to reach USD 4,274.3 million by 2030, demonstrating a compound annual growth rate (CAGR) of 4.4% from 2025 to 2030.n*   France accounted for 3.2% of the global amusement parks market in 2024.n*   In Europe, the French amusement parks market is expected to lead in terms of revenue by 2030 and is projected to be the fastest-growing regional market.nn**",
                  "annotations": [
                    {
                      "title": "grandviewresearch.com",
                      "url": "https://vertexaisearch.cloud.google.com/grounding-api-redirect/AUZIYQE4kVSlqaUzZmAm6xWUASs0ppDa88LJ3WrthsLwppW3uzY6ROF9gQAeT1Q85e5W4etkjCovvSU8ygGEPgCcs0eC46cdz8IOjbyGJXbAvC5UPmsL2MWW5nCMa7JNk7rsMimpbiBDyXzpO_YZCecF-egFpoGFq3UN-GQ8wlYKgpZ7Z7kP8uHLWc2eOw=="
                    }
                  ]
                }
              ]
            }
          ]
        }
      ]
    }
  ]
}