GPT Responses (All Models, Full Parameters)

curl --request POST \
  --url https://direct.evolink.ai/v1/responses \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-5.6-sol",
  "input": "Search for AI news from the past week and summarize it in three sentences.",
  "instructions": "You are a concise assistant. Answer in no more than three sentences.",
  "stream": false,
  "max_output_tokens": 2048,
  "reasoning": {
    "effort": "medium",
    "summary": "auto",
    "mode": "standard",
    "context": "current_turn"
  },
  "text": {
    "format": {},
    "verbosity": "medium"
  },
  "tools": [
    {
      "type": "web_search"
    }
  ],
  "max_tool_calls": 5,
  "parallel_tool_calls": true,
  "previous_response_id": "resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5",
  "store": true,
  "include": [
    "reasoning.encrypted_content"
  ],
  "temperature": 0.7,
  "top_p": 0.9,
  "top_logprobs": 2,
  "frequency_penalty": 0.5,
  "presence_penalty": 0.5,
  "truncation": "auto",
  "context_management": [
    {}
  ],
  "prompt_cache_key": "app-agent-v1",
  "prompt_cache_retention": "in_memory",
  "prompt": {
    "id": "<string>",
    "version": "<string>",
    "variables": {}
  },
  "metadata": {
    "trace_id": "abc-123"
  },
  "safety_identifier": "user-1024",
  "user": "user-1024"
}
'

import requests

url = "https://direct.evolink.ai/v1/responses"

payload = {
    "model": "gpt-5.6-sol",
    "input": "Search for AI news from the past week and summarize it in three sentences.",
    "instructions": "You are a concise assistant. Answer in no more than three sentences.",
    "stream": False,
    "max_output_tokens": 2048,
    "reasoning": {
        "effort": "medium",
        "summary": "auto",
        "mode": "standard",
        "context": "current_turn"
    },
    "text": {
        "format": {},
        "verbosity": "medium"
    },
    "tools": [{ "type": "web_search" }],
    "max_tool_calls": 5,
    "parallel_tool_calls": True,
    "previous_response_id": "resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5",
    "store": True,
    "include": ["reasoning.encrypted_content"],
    "temperature": 0.7,
    "top_p": 0.9,
    "top_logprobs": 2,
    "frequency_penalty": 0.5,
    "presence_penalty": 0.5,
    "truncation": "auto",
    "context_management": [{}],
    "prompt_cache_key": "app-agent-v1",
    "prompt_cache_retention": "in_memory",
    "prompt": {
        "id": "<string>",
        "version": "<string>",
        "variables": {}
    },
    "metadata": { "trace_id": "abc-123" },
    "safety_identifier": "user-1024",
    "user": "user-1024"
}
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({
    model: 'gpt-5.6-sol',
    input: 'Search for AI news from the past week and summarize it in three sentences.',
    instructions: 'You are a concise assistant. Answer in no more than three sentences.',
    stream: false,
    max_output_tokens: 2048,
    reasoning: {effort: 'medium', summary: 'auto', mode: 'standard', context: 'current_turn'},
    text: {format: {}, verbosity: 'medium'},
    tools: [{type: 'web_search'}],
    max_tool_calls: 5,
    parallel_tool_calls: true,
    previous_response_id: 'resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5',
    store: true,
    include: ['reasoning.encrypted_content'],
    temperature: 0.7,
    top_p: 0.9,
    top_logprobs: 2,
    frequency_penalty: 0.5,
    presence_penalty: 0.5,
    truncation: 'auto',
    context_management: [{}],
    prompt_cache_key: 'app-agent-v1',
    prompt_cache_retention: 'in_memory',
    prompt: {id: '<string>', version: '<string>', variables: {}},
    metadata: {trace_id: 'abc-123'},
    safety_identifier: 'user-1024',
    user: 'user-1024'
  })
};

fetch('https://direct.evolink.ai/v1/responses', 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://direct.evolink.ai/v1/responses",
  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([
    'model' => 'gpt-5.6-sol',
    'input' => 'Search for AI news from the past week and summarize it in three sentences.',
    'instructions' => 'You are a concise assistant. Answer in no more than three sentences.',
    'stream' => false,
    'max_output_tokens' => 2048,
    'reasoning' => [
        'effort' => 'medium',
        'summary' => 'auto',
        'mode' => 'standard',
        'context' => 'current_turn'
    ],
    'text' => [
        'format' => [
                
        ],
        'verbosity' => 'medium'
    ],
    'tools' => [
        [
                'type' => 'web_search'
        ]
    ],
    'max_tool_calls' => 5,
    'parallel_tool_calls' => true,
    'previous_response_id' => 'resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5',
    'store' => true,
    'include' => [
        'reasoning.encrypted_content'
    ],
    'temperature' => 0.7,
    'top_p' => 0.9,
    'top_logprobs' => 2,
    'frequency_penalty' => 0.5,
    'presence_penalty' => 0.5,
    'truncation' => 'auto',
    'context_management' => [
        [
                
        ]
    ],
    'prompt_cache_key' => 'app-agent-v1',
    'prompt_cache_retention' => 'in_memory',
    'prompt' => [
        'id' => '<string>',
        'version' => '<string>',
        'variables' => [
                
        ]
    ],
    'metadata' => [
        'trace_id' => 'abc-123'
    ],
    'safety_identifier' => 'user-1024',
    'user' => 'user-1024'
  ]),
  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://direct.evolink.ai/v1/responses"

	payload := strings.NewReader("{\n  \"model\": \"gpt-5.6-sol\",\n  \"input\": \"Search for AI news from the past week and summarize it in three sentences.\",\n  \"instructions\": \"You are a concise assistant. Answer in no more than three sentences.\",\n  \"stream\": false,\n  \"max_output_tokens\": 2048,\n  \"reasoning\": {\n    \"effort\": \"medium\",\n    \"summary\": \"auto\",\n    \"mode\": \"standard\",\n    \"context\": \"current_turn\"\n  },\n  \"text\": {\n    \"format\": {},\n    \"verbosity\": \"medium\"\n  },\n  \"tools\": [\n    {\n      \"type\": \"web_search\"\n    }\n  ],\n  \"max_tool_calls\": 5,\n  \"parallel_tool_calls\": true,\n  \"previous_response_id\": \"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5\",\n  \"store\": true,\n  \"include\": [\n    \"reasoning.encrypted_content\"\n  ],\n  \"temperature\": 0.7,\n  \"top_p\": 0.9,\n  \"top_logprobs\": 2,\n  \"frequency_penalty\": 0.5,\n  \"presence_penalty\": 0.5,\n  \"truncation\": \"auto\",\n  \"context_management\": [\n    {}\n  ],\n  \"prompt_cache_key\": \"app-agent-v1\",\n  \"prompt_cache_retention\": \"in_memory\",\n  \"prompt\": {\n    \"id\": \"<string>\",\n    \"version\": \"<string>\",\n    \"variables\": {}\n  },\n  \"metadata\": {\n    \"trace_id\": \"abc-123\"\n  },\n  \"safety_identifier\": \"user-1024\",\n  \"user\": \"user-1024\"\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://direct.evolink.ai/v1/responses")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"gpt-5.6-sol\",\n  \"input\": \"Search for AI news from the past week and summarize it in three sentences.\",\n  \"instructions\": \"You are a concise assistant. Answer in no more than three sentences.\",\n  \"stream\": false,\n  \"max_output_tokens\": 2048,\n  \"reasoning\": {\n    \"effort\": \"medium\",\n    \"summary\": \"auto\",\n    \"mode\": \"standard\",\n    \"context\": \"current_turn\"\n  },\n  \"text\": {\n    \"format\": {},\n    \"verbosity\": \"medium\"\n  },\n  \"tools\": [\n    {\n      \"type\": \"web_search\"\n    }\n  ],\n  \"max_tool_calls\": 5,\n  \"parallel_tool_calls\": true,\n  \"previous_response_id\": \"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5\",\n  \"store\": true,\n  \"include\": [\n    \"reasoning.encrypted_content\"\n  ],\n  \"temperature\": 0.7,\n  \"top_p\": 0.9,\n  \"top_logprobs\": 2,\n  \"frequency_penalty\": 0.5,\n  \"presence_penalty\": 0.5,\n  \"truncation\": \"auto\",\n  \"context_management\": [\n    {}\n  ],\n  \"prompt_cache_key\": \"app-agent-v1\",\n  \"prompt_cache_retention\": \"in_memory\",\n  \"prompt\": {\n    \"id\": \"<string>\",\n    \"version\": \"<string>\",\n    \"variables\": {}\n  },\n  \"metadata\": {\n    \"trace_id\": \"abc-123\"\n  },\n  \"safety_identifier\": \"user-1024\",\n  \"user\": \"user-1024\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://direct.evolink.ai/v1/responses")

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  \"model\": \"gpt-5.6-sol\",\n  \"input\": \"Search for AI news from the past week and summarize it in three sentences.\",\n  \"instructions\": \"You are a concise assistant. Answer in no more than three sentences.\",\n  \"stream\": false,\n  \"max_output_tokens\": 2048,\n  \"reasoning\": {\n    \"effort\": \"medium\",\n    \"summary\": \"auto\",\n    \"mode\": \"standard\",\n    \"context\": \"current_turn\"\n  },\n  \"text\": {\n    \"format\": {},\n    \"verbosity\": \"medium\"\n  },\n  \"tools\": [\n    {\n      \"type\": \"web_search\"\n    }\n  ],\n  \"max_tool_calls\": 5,\n  \"parallel_tool_calls\": true,\n  \"previous_response_id\": \"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5\",\n  \"store\": true,\n  \"include\": [\n    \"reasoning.encrypted_content\"\n  ],\n  \"temperature\": 0.7,\n  \"top_p\": 0.9,\n  \"top_logprobs\": 2,\n  \"frequency_penalty\": 0.5,\n  \"presence_penalty\": 0.5,\n  \"truncation\": \"auto\",\n  \"context_management\": [\n    {}\n  ],\n  \"prompt_cache_key\": \"app-agent-v1\",\n  \"prompt_cache_retention\": \"in_memory\",\n  \"prompt\": {\n    \"id\": \"<string>\",\n    \"version\": \"<string>\",\n    \"variables\": {}\n  },\n  \"metadata\": {\n    \"trace_id\": \"abc-123\"\n  },\n  \"safety_identifier\": \"user-1024\",\n  \"user\": \"user-1024\"\n}"

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

{
  "id": "resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5",
  "object": "response",
  "status": "completed",
  "model": "gpt-5.6-sol",
  "created_at": 1786705221,
  "output": [
    {
      "id": "<string>",
      "type": "web_search_call",
      "status": "completed",
      "content": [
        {}
      ],
      "encrypted_content": "<string>"
    }
  ],
  "incomplete_details": {},
  "usage": {
    "input_tokens": 18,
    "output_tokens": 42,
    "total_tokens": 60,
    "input_tokens_details": {
      "cached_tokens": 0,
      "cache_write_tokens": 0
    },
    "output_tokens_details": {
      "reasoning_tokens": 16
    }
  },
  "metadata": {}
}

{
  "error": {
    "code": 400,
    "message": "Invalid value: '__bogus__'. Supported values are: 'auto' and 'disabled'.",
    "type": "invalid_request_error",
    "param": "truncation"
  }
}

{
  "error": {
    "code": 401,
    "message": "Invalid or expired token",
    "type": "authentication_error"
  }
}

{
  "error": {
    "code": 402,
    "message": "Insufficient quota",
    "type": "insufficient_quota_error",
    "fallback_suggestion": "https://evolink.ai/dashboard/billing"
  }
}

{
  "error": {
    "code": 429,
    "message": "Rate limit exceeded",
    "type": "rate_limit_error",
    "fallback_suggestion": "retry after 60 seconds"
  }
}

{
  "error": {
    "code": 500,
    "message": "Internal server error",
    "type": "internal_server_error",
    "fallback_suggestion": "try again later"
  }
}

{
  "error": {
    "code": 503,
    "message": "Service temporarily unavailable",
    "type": "service_unavailable_error",
    "fallback_suggestion": "retry after 30 seconds"
  }
}

Responses API

GPT All-Model API - Responses Reference

OpenAI-compatible Responses API for GPT series text models; select the specific model via model (see the comparison table on the model parameter for all allowed values)
The whole series consists of reasoning models, with reasoning depth controlled by reasoning.effort; reasoning tokens are billed as output tokens
Prompt caching applies automatically: cached input tokens are billed at the lower cached rate
Supports both synchronous and streaming (SSE) modes
Server-side tools: web_search (web search), code_interpreter (code execution), file_search (file search)
Plain function tools (client-side function calls) are supported as well
Multi-turn conversations can be chained with previous_response_id
Note Model support varies for some parameters; see the per-parameter notes below

POST

responses

GPT Responses (All Models, Full Parameters)

curl --request POST \
  --url https://direct.evolink.ai/v1/responses \
  --header 'Authorization: Bearer <token>' \
  --header 'Content-Type: application/json' \
  --data '
{
  "model": "gpt-5.6-sol",
  "input": "Search for AI news from the past week and summarize it in three sentences.",
  "instructions": "You are a concise assistant. Answer in no more than three sentences.",
  "stream": false,
  "max_output_tokens": 2048,
  "reasoning": {
    "effort": "medium",
    "summary": "auto",
    "mode": "standard",
    "context": "current_turn"
  },
  "text": {
    "format": {},
    "verbosity": "medium"
  },
  "tools": [
    {
      "type": "web_search"
    }
  ],
  "max_tool_calls": 5,
  "parallel_tool_calls": true,
  "previous_response_id": "resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5",
  "store": true,
  "include": [
    "reasoning.encrypted_content"
  ],
  "temperature": 0.7,
  "top_p": 0.9,
  "top_logprobs": 2,
  "frequency_penalty": 0.5,
  "presence_penalty": 0.5,
  "truncation": "auto",
  "context_management": [
    {}
  ],
  "prompt_cache_key": "app-agent-v1",
  "prompt_cache_retention": "in_memory",
  "prompt": {
    "id": "<string>",
    "version": "<string>",
    "variables": {}
  },
  "metadata": {
    "trace_id": "abc-123"
  },
  "safety_identifier": "user-1024",
  "user": "user-1024"
}
'

import requests

url = "https://direct.evolink.ai/v1/responses"

payload = {
    "model": "gpt-5.6-sol",
    "input": "Search for AI news from the past week and summarize it in three sentences.",
    "instructions": "You are a concise assistant. Answer in no more than three sentences.",
    "stream": False,
    "max_output_tokens": 2048,
    "reasoning": {
        "effort": "medium",
        "summary": "auto",
        "mode": "standard",
        "context": "current_turn"
    },
    "text": {
        "format": {},
        "verbosity": "medium"
    },
    "tools": [{ "type": "web_search" }],
    "max_tool_calls": 5,
    "parallel_tool_calls": True,
    "previous_response_id": "resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5",
    "store": True,
    "include": ["reasoning.encrypted_content"],
    "temperature": 0.7,
    "top_p": 0.9,
    "top_logprobs": 2,
    "frequency_penalty": 0.5,
    "presence_penalty": 0.5,
    "truncation": "auto",
    "context_management": [{}],
    "prompt_cache_key": "app-agent-v1",
    "prompt_cache_retention": "in_memory",
    "prompt": {
        "id": "<string>",
        "version": "<string>",
        "variables": {}
    },
    "metadata": { "trace_id": "abc-123" },
    "safety_identifier": "user-1024",
    "user": "user-1024"
}
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({
    model: 'gpt-5.6-sol',
    input: 'Search for AI news from the past week and summarize it in three sentences.',
    instructions: 'You are a concise assistant. Answer in no more than three sentences.',
    stream: false,
    max_output_tokens: 2048,
    reasoning: {effort: 'medium', summary: 'auto', mode: 'standard', context: 'current_turn'},
    text: {format: {}, verbosity: 'medium'},
    tools: [{type: 'web_search'}],
    max_tool_calls: 5,
    parallel_tool_calls: true,
    previous_response_id: 'resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5',
    store: true,
    include: ['reasoning.encrypted_content'],
    temperature: 0.7,
    top_p: 0.9,
    top_logprobs: 2,
    frequency_penalty: 0.5,
    presence_penalty: 0.5,
    truncation: 'auto',
    context_management: [{}],
    prompt_cache_key: 'app-agent-v1',
    prompt_cache_retention: 'in_memory',
    prompt: {id: '<string>', version: '<string>', variables: {}},
    metadata: {trace_id: 'abc-123'},
    safety_identifier: 'user-1024',
    user: 'user-1024'
  })
};

fetch('https://direct.evolink.ai/v1/responses', 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://direct.evolink.ai/v1/responses",
  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([
    'model' => 'gpt-5.6-sol',
    'input' => 'Search for AI news from the past week and summarize it in three sentences.',
    'instructions' => 'You are a concise assistant. Answer in no more than three sentences.',
    'stream' => false,
    'max_output_tokens' => 2048,
    'reasoning' => [
        'effort' => 'medium',
        'summary' => 'auto',
        'mode' => 'standard',
        'context' => 'current_turn'
    ],
    'text' => [
        'format' => [
                
        ],
        'verbosity' => 'medium'
    ],
    'tools' => [
        [
                'type' => 'web_search'
        ]
    ],
    'max_tool_calls' => 5,
    'parallel_tool_calls' => true,
    'previous_response_id' => 'resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5',
    'store' => true,
    'include' => [
        'reasoning.encrypted_content'
    ],
    'temperature' => 0.7,
    'top_p' => 0.9,
    'top_logprobs' => 2,
    'frequency_penalty' => 0.5,
    'presence_penalty' => 0.5,
    'truncation' => 'auto',
    'context_management' => [
        [
                
        ]
    ],
    'prompt_cache_key' => 'app-agent-v1',
    'prompt_cache_retention' => 'in_memory',
    'prompt' => [
        'id' => '<string>',
        'version' => '<string>',
        'variables' => [
                
        ]
    ],
    'metadata' => [
        'trace_id' => 'abc-123'
    ],
    'safety_identifier' => 'user-1024',
    'user' => 'user-1024'
  ]),
  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://direct.evolink.ai/v1/responses"

	payload := strings.NewReader("{\n  \"model\": \"gpt-5.6-sol\",\n  \"input\": \"Search for AI news from the past week and summarize it in three sentences.\",\n  \"instructions\": \"You are a concise assistant. Answer in no more than three sentences.\",\n  \"stream\": false,\n  \"max_output_tokens\": 2048,\n  \"reasoning\": {\n    \"effort\": \"medium\",\n    \"summary\": \"auto\",\n    \"mode\": \"standard\",\n    \"context\": \"current_turn\"\n  },\n  \"text\": {\n    \"format\": {},\n    \"verbosity\": \"medium\"\n  },\n  \"tools\": [\n    {\n      \"type\": \"web_search\"\n    }\n  ],\n  \"max_tool_calls\": 5,\n  \"parallel_tool_calls\": true,\n  \"previous_response_id\": \"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5\",\n  \"store\": true,\n  \"include\": [\n    \"reasoning.encrypted_content\"\n  ],\n  \"temperature\": 0.7,\n  \"top_p\": 0.9,\n  \"top_logprobs\": 2,\n  \"frequency_penalty\": 0.5,\n  \"presence_penalty\": 0.5,\n  \"truncation\": \"auto\",\n  \"context_management\": [\n    {}\n  ],\n  \"prompt_cache_key\": \"app-agent-v1\",\n  \"prompt_cache_retention\": \"in_memory\",\n  \"prompt\": {\n    \"id\": \"<string>\",\n    \"version\": \"<string>\",\n    \"variables\": {}\n  },\n  \"metadata\": {\n    \"trace_id\": \"abc-123\"\n  },\n  \"safety_identifier\": \"user-1024\",\n  \"user\": \"user-1024\"\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://direct.evolink.ai/v1/responses")
  .header("Authorization", "Bearer <token>")
  .header("Content-Type", "application/json")
  .body("{\n  \"model\": \"gpt-5.6-sol\",\n  \"input\": \"Search for AI news from the past week and summarize it in three sentences.\",\n  \"instructions\": \"You are a concise assistant. Answer in no more than three sentences.\",\n  \"stream\": false,\n  \"max_output_tokens\": 2048,\n  \"reasoning\": {\n    \"effort\": \"medium\",\n    \"summary\": \"auto\",\n    \"mode\": \"standard\",\n    \"context\": \"current_turn\"\n  },\n  \"text\": {\n    \"format\": {},\n    \"verbosity\": \"medium\"\n  },\n  \"tools\": [\n    {\n      \"type\": \"web_search\"\n    }\n  ],\n  \"max_tool_calls\": 5,\n  \"parallel_tool_calls\": true,\n  \"previous_response_id\": \"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5\",\n  \"store\": true,\n  \"include\": [\n    \"reasoning.encrypted_content\"\n  ],\n  \"temperature\": 0.7,\n  \"top_p\": 0.9,\n  \"top_logprobs\": 2,\n  \"frequency_penalty\": 0.5,\n  \"presence_penalty\": 0.5,\n  \"truncation\": \"auto\",\n  \"context_management\": [\n    {}\n  ],\n  \"prompt_cache_key\": \"app-agent-v1\",\n  \"prompt_cache_retention\": \"in_memory\",\n  \"prompt\": {\n    \"id\": \"<string>\",\n    \"version\": \"<string>\",\n    \"variables\": {}\n  },\n  \"metadata\": {\n    \"trace_id\": \"abc-123\"\n  },\n  \"safety_identifier\": \"user-1024\",\n  \"user\": \"user-1024\"\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://direct.evolink.ai/v1/responses")

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  \"model\": \"gpt-5.6-sol\",\n  \"input\": \"Search for AI news from the past week and summarize it in three sentences.\",\n  \"instructions\": \"You are a concise assistant. Answer in no more than three sentences.\",\n  \"stream\": false,\n  \"max_output_tokens\": 2048,\n  \"reasoning\": {\n    \"effort\": \"medium\",\n    \"summary\": \"auto\",\n    \"mode\": \"standard\",\n    \"context\": \"current_turn\"\n  },\n  \"text\": {\n    \"format\": {},\n    \"verbosity\": \"medium\"\n  },\n  \"tools\": [\n    {\n      \"type\": \"web_search\"\n    }\n  ],\n  \"max_tool_calls\": 5,\n  \"parallel_tool_calls\": true,\n  \"previous_response_id\": \"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5\",\n  \"store\": true,\n  \"include\": [\n    \"reasoning.encrypted_content\"\n  ],\n  \"temperature\": 0.7,\n  \"top_p\": 0.9,\n  \"top_logprobs\": 2,\n  \"frequency_penalty\": 0.5,\n  \"presence_penalty\": 0.5,\n  \"truncation\": \"auto\",\n  \"context_management\": [\n    {}\n  ],\n  \"prompt_cache_key\": \"app-agent-v1\",\n  \"prompt_cache_retention\": \"in_memory\",\n  \"prompt\": {\n    \"id\": \"<string>\",\n    \"version\": \"<string>\",\n    \"variables\": {}\n  },\n  \"metadata\": {\n    \"trace_id\": \"abc-123\"\n  },\n  \"safety_identifier\": \"user-1024\",\n  \"user\": \"user-1024\"\n}"

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

{
  "id": "resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5",
  "object": "response",
  "status": "completed",
  "model": "gpt-5.6-sol",
  "created_at": 1786705221,
  "output": [
    {
      "id": "<string>",
      "type": "web_search_call",
      "status": "completed",
      "content": [
        {}
      ],
      "encrypted_content": "<string>"
    }
  ],
  "incomplete_details": {},
  "usage": {
    "input_tokens": 18,
    "output_tokens": 42,
    "total_tokens": 60,
    "input_tokens_details": {
      "cached_tokens": 0,
      "cache_write_tokens": 0
    },
    "output_tokens_details": {
      "reasoning_tokens": 16
    }
  },
  "metadata": {}
}

{
  "error": {
    "code": 400,
    "message": "Invalid value: '__bogus__'. Supported values are: 'auto' and 'disabled'.",
    "type": "invalid_request_error",
    "param": "truncation"
  }
}

{
  "error": {
    "code": 401,
    "message": "Invalid or expired token",
    "type": "authentication_error"
  }
}

{
  "error": {
    "code": 402,
    "message": "Insufficient quota",
    "type": "insufficient_quota_error",
    "fallback_suggestion": "https://evolink.ai/dashboard/billing"
  }
}

{
  "error": {
    "code": 429,
    "message": "Rate limit exceeded",
    "type": "rate_limit_error",
    "fallback_suggestion": "retry after 60 seconds"
  }
}

{
  "error": {
    "code": 500,
    "message": "Internal server error",
    "type": "internal_server_error",
    "fallback_suggestion": "try again later"
  }
}

{
  "error": {
    "code": 503,
    "message": "Service temporarily unavailable",
    "type": "service_unavailable_error",
    "fallback_suggestion": "retry after 30 seconds"
  }
}

BaseURL: The default BaseURL is https://direct.evolink.ai, which has better support for text models and long-lived connections. https://api.evolink.ai is the primary endpoint for multimodal services and serves as a fallback address for text models.

Server-side tools (web_search, code_interpreter, file_search, mcp) run on the server, so results do not need to be sent back by the client, and they are only available on this API. The Chat Completions endpoint supports regular function tool calling only.

Note This API supports synchronous and streaming modes only: the background: true asynchronous mode is not supported, and there are no endpoints for retrieving, cancelling or deleting a response by its ID. For long-running generations, use stream: true to keep the connection open.The image_generation tool is not available on this model series; for image generation, use the image series model APIs.

Multi-turn conversations: pass the id returned by the previous turn as previous_response_id on the next turn to continue the context. Responses have a retention period; once it expires the ID is no longer valid and the request is handled as a new conversation. For scenarios with strict context-accuracy requirements, maintaining the full input history yourself is recommended.

Authorizations

Authorization

string

header

required

##All APIs require Bearer Token authentication##

Get API Key:

Visit API Key Management Page to get your API Key

Add to request header:

Authorization: Bearer YOUR_API_KEY

Body

application/json

model

enum<string>

required

Model to call:

Model ID	Context window	Positioning
`gpt-5.6-sol`	1,050,000	GPT-5.6 family, frontier reasoning
`gpt-5.6-terra`	1,050,000	GPT-5.6 family, balanced production
`gpt-5.6-luna`	1,050,000	GPT-5.6 family, high throughput and cost control
`gpt-5.5`	400,000	General-purpose reasoning model
`gpt-5.4`	128,000	General-purpose reasoning model
`gpt-5.2`	400,000	General-purpose reasoning model
`gpt-5.1`	400,000	General-purpose reasoning model

Available options:

gpt-5.6-sol,

gpt-5.6-terra,

gpt-5.6-luna,

gpt-5.5,

gpt-5.4,

gpt-5.2,

gpt-5.1

Example:

"gpt-5.6-sol"

input

required

Model input: a plain string, or an array of input items.

The content of an input item supports two block types: input_text (text) and input_image (image):

"input": [
  {
    "role": "user",
    "content": [
      { "type": "input_text", "text": "What is in this image?" },
      {
        "type": "input_image",
        "image_url": "https://example.com/photo.png",
        "detail": "auto"
      }
    ]
  }
]

Image

Pass the public URL of the image in image_url
image_url must be a string; writing it as { "url": "..." } returns 400
detail is a sibling of image_url (not nested inside it): auto (default) / low / high / original
The image must be downloadable, otherwise 400 is returned

Tool results

The array can also carry back tool result items from the previous turn, such as function_call_output

Note The block types on this API differ from those on the Chat Completions API (which uses text / image_url). They cannot be mixed; using the wrong ones returns 400.

Example:

"Search for AI news from the past week and summarize it in three sentences."

instructions

string

System-level instructions, equivalent to inserting a system message at the very beginning of input. When continuing a conversation with previous_response_id, this parameter is not inherited from the previous turn and must be passed on every turn.

Example:

"You are a concise assistant. Answer in no more than three sentences."

stream

boolean

default:false

Whether to return a streaming response (SSE events, ending with response.completed). Default false.

Example:

false

max_output_tokens

integer

Maximum number of tokens to generate (including reasoning tokens). When the limit is reached, status is incomplete.

Example:

2048

reasoning

object

Reasoning control.

The allowed values of effort (reasoning depth) vary by model:

Model	Allowed values
`gpt-5.6-sol` / `gpt-5.6-terra` / `gpt-5.6-luna`	`none`, `low`, `medium`, `high`, `xhigh`, `max`
`gpt-5.5` / `gpt-5.4` / `gpt-5.2`	`none`, `low`, `medium`, `high`, `xhigh`
`gpt-5.1`	`none`, `low`, `medium`, `high`

summary (reasoning summary): auto / concise / detailed, available across the series. Once enabled, a reasoning item appears in output.

mode (reasoning mode): standard / pro, supported only by the gpt-5.6 family.

context (reasoning context scope): auto / current_turn / all_turns, supported only by the gpt-5.6 family.

Reasoning tokens are billed as output tokens and counted in usage.output_tokens_details.reasoning_tokens.

Show child attributes

text

object

Output text control:

format: {"type": "text"} (default), {"type": "json_object"}, or {"type": "json_schema", "name": "...", "schema": {...}, "strict": true} for structured output
verbosity: low / medium / high, controls how detailed the answer is

Show child attributes

tools

object[]

Tool declarations. Server-side tools run on the server, so results do not need to be sent back by the client:

Tool type	Capability
`web_search`	Searches the web and browses pages (alias `web_search_preview`)
`code_interpreter`	Runs code in a sandbox; requires `"container": {"type": "auto"}`
`file_search`	Searches an existing vector store; requires `vector_store_ids`
`mcp`	Connects to a remote MCP service; requires `server_label` and `server_url`

Plain function tools (client-side function calls) are supported as well.

Note image_generation is not available on this model series; use the image series model APIs instead.

Show child attributes

Example:

[{ "type": "web_search" }]

tool_choice

Controls tool selection: "auto" (default) / "none" / "required", or an object pinning a specific tool, e.g. {"type": "web_search"}.

Available options:

none,

auto,

required

max_tool_calls

integer

Maximum total number of tool calls allowed in this response.

Example:

5

parallel_tool_calls

boolean

default:true

Whether the model may call multiple tools in parallel within one turn. Defaults to true.

Note Only the gpt-5.6 family and gpt-5.5 support setting it to false; on gpt-5.4 / gpt-5.2 / gpt-5.1 this parameter has no effect and always behaves as true.

Example:

true

previous_response_id

string

The id of the previous response, used to chain multi-turn conversations without re-uploading the history.

Note Must be used together with store: true (the default). Responses have a retention period; once it expires the ID is no longer valid, and the request is handled as a new conversation without inheriting context. For scenarios with strict context-accuracy requirements, maintaining the full input history yourself is recommended.

Example:

"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5"

store

boolean

default:true

Whether to store this response on the server; only stored responses can be referenced by previous_response_id. Defaults to true.

Note Only the gpt-5.6 family and gpt-5.5 support setting it to false; on gpt-5.4 / gpt-5.2 / gpt-5.1 this parameter has no effect and always behaves as true. If you do not want responses stored, choose a model that supports turning it off.

Example:

true

include

string[]

Additional content to return in the response. Allowed values:

reasoning.encrypted_content
message.output_text.logprobs
web_search_call.results
web_search_call.action.sources
file_search_call.results
code_interpreter_call.outputs
message.input_image.image_url
computer_call_output.output.image_url

Example:

["reasoning.encrypted_content"]

temperature

number

Sampling temperature, ranging from 0 to 2. Lower values make the output more deterministic.

Note On gpt-5.4 / gpt-5.2 / gpt-5.1 the value 0 has no effect (it is treated as unset and falls back to the default 1); for more deterministic output, use a value greater than 0 such as 0.01.

Required range: 0 <= x <= 2

Example:

0.7

top_p

number

Nucleus sampling parameter, ranging from 0 to 1. Adjusting it together with temperature is not recommended.

Required range: 0 <= x <= 1

Example:

0.9

top_logprobs

integer

Number of candidate tokens returned at each position, ranging from 0 to 20; must be used together with include: ["message.output_text.logprobs"].

Note Supported only by the gpt-5.6 family and gpt-5.5; other models do not support this parameter.

Required range: 0 <= x <= 20

Example:

2

frequency_penalty

number

Frequency penalty, ranging from -2 to 2, reducing the likelihood of repeated content.

Note Supported only by the gpt-5.6 family; other models do not support this parameter.

Required range: -2 <= x <= 2

Example:

0.5

presence_penalty

number

Presence penalty, ranging from -2 to 2, encouraging the model to talk about new topics.

Note Supported only by the gpt-5.6 family; other models do not support this parameter.

Required range: -2 <= x <= 2

Example:

0.5

truncation

enum<string>

default:disabled

How to handle context that exceeds the window: disabled (default, returns an error) or auto (automatically truncates the middle of the context).

Available options:

auto,

disabled

Example:

"auto"

context_management

object[]

Automatic compaction configuration for long conversations, for example [{"type": "compaction", "compact_threshold": 100000}]: the history is compacted automatically once the context exceeds the threshold.

Note Supported only by the gpt-5.6 family; other models do not support this parameter.

prompt_cache_key

string

Cache grouping key. Passing the same value for requests that share the same prefix improves the prompt cache hit rate.

Example:

"app-agent-v1"

prompt_cache_retention

enum<string>

Prompt cache retention policy: in_memory (default) or 24h (extends how long the cache is retained).

Available options:

in_memory,

24h

Example:

"in_memory"

prompt

object

References an already created prompt template, in the form {"id": "pmpt_xxx", "version": "1", "variables": {...}}.

Show child attributes

metadata

object

Custom key-value pairs returned as-is with the response, convenient for tagging on the business side. Both keys and values are strings.

Example:

{ "trace_id": "abc-123" }

safety_identifier

string

Stable identifier of the end user, used for abuse tracking.

Note Supported only by the gpt-5.6 family; other models do not support this parameter.

Example:

"user-1024"

user

string

End-user identifier, used to distinguish the source of calls.

Example:

"user-1024"

Response

Response generated successfully (JSON object, or an SSE event stream ending with response.completed when stream=true)

string

Unique ID of this response, which can be used as previous_response_id for the next turn

Example:

"resp_0f5c2b2c20c39e8a006a7ef545443081979e478b10927984b5"

object

enum<string>

Response type

Available options:

response

Example:

"response"

status

enum<string>

Response status: completed for a normal ending, incomplete when generation stopped early for reasons such as reaching max_output_tokens, failed when generation failed

Available options:

completed,

incomplete,

failed

Example:

"completed"

model

string

Actual model name used

Example:

"gpt-5.6-sol"

created_at

integer

Creation timestamp

Example:

1786705221

output

object[]

Output items in generation order: the reasoning item (reasoning summary / encrypted reasoning content), tool call items (such as web_search_call and code_interpreter_call), and finally the message item containing output_text content.

Show child attributes

incomplete_details

object

Explains the reason when status is incomplete

usage

object

Token usage statistics. Prompt caching applies automatically, and cached input tokens are billed at the lower cached rate.

Show child attributes

metadata

object

Custom key-value pairs passed in the request, returned as-is

Image Series

Video Series

Audio Series

Text Series

Account Management

Task Management

File Management

GPT All-Model API - Responses Reference

Authorizations

Body

Response