OpenAI Responses API

curl -X POST https://aireiter.com/api/v1/responses \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "input": "Write a one-sentence bedtime story about a unicorn.",
    "stream": true
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://aireiter.com/api/v1"
)

response = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    input="Write a one-sentence bedtime story about a unicorn."
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://aireiter.com/api/v1",
});

const response = await client.responses.create({
  model: "claude-sonnet-4-5-20250929",
  input: "Write a one-sentence bedtime story about a unicorn.",
});

console.log(response.output_text);

package main

import (
  "bytes"
  "encoding/json"
  "fmt"
  "net/http"
)

func main() {
  payload := map[string]interface{}{
    "model":  "claude-sonnet-4-5-20250929",
    "input":  "Write a one-sentence bedtime story about a unicorn.",
    "stream": false,
  }
  body, _ := json.Marshal(payload)

  req, _ := http.NewRequest("POST", "https://aireiter.com/api/v1/responses", bytes.NewBuffer(body))
  req.Header.Set("Authorization", "Bearer YOUR_API_KEY")
  req.Header.Set("Content-Type", "application/json")

  resp, _ := http.DefaultClient.Do(req)
  defer resp.Body.Close()

  var result map[string]interface{}
  json.NewDecoder(resp.Body).Decode(&result)
  fmt.Println(result)
}

import com.fasterxml.jackson.databind.ObjectMapper;
import java.net.URI;
import java.net.http.*;
import java.util.Map;

public class ResponsesExample {
  public static void main(String[] args) throws Exception {
    var payload = Map.of(
      "model", "claude-sonnet-4-5-20250929",
      "input", "Write a one-sentence bedtime story about a unicorn.",
      "stream", false
    );
    var body = new ObjectMapper().writeValueAsString(payload);
    var request = HttpRequest.newBuilder()
      .uri(URI.create("https://aireiter.com/api/v1/responses"))
      .header("Authorization", "Bearer YOUR_API_KEY")
      .header("Content-Type", "application/json")
      .POST(HttpRequest.BodyPublishers.ofString(body))
      .build();
    var response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
    System.out.println(response.body());
  }
}

<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://aireiter.com/api/v1/responses', [
  'headers' => [
    'Authorization' => 'Bearer YOUR_API_KEY',
    'Content-Type'  => 'application/json',
  ],
  'json' => [
    'model'  => 'claude-sonnet-4-5-20250929',
    'input'  => 'Write a one-sentence bedtime story about a unicorn.',
    'stream' => false,
  ],
]);
echo $response->getBody();

require 'net/http'
require 'json'

uri = URI('https://aireiter.com/api/v1/responses')
req = Net::HTTP::Post.new(uri, {
  'Authorization' => 'Bearer YOUR_API_KEY',
  'Content-Type'  => 'application/json'
})
req.body = {
  model:  'claude-sonnet-4-5-20250929',
  input:  'Write a one-sentence bedtime story about a unicorn.',
  stream: false
}.to_json

res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |h| h.request(req) }
puts res.body

import Foundation

let payload: [String: Any] = [
  "model":  "claude-sonnet-4-5-20250929",
  "input":  "Write a one-sentence bedtime story about a unicorn.",
  "stream": false
]

var request = URLRequest(url: URL(string: "https://aireiter.com/api/v1/responses")!)
request.httpMethod = "POST"
request.setValue("Bearer YOUR_API_KEY", forHTTPHeaderField: "Authorization")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try! JSONSerialization.data(withJSONObject: payload)

URLSession.shared.dataTask(with: request) { data, _, _ in
  if let data = data { print(String(data: data, encoding: .utf8)!) }
}.resume()

using System.Net.Http;
using System.Net.Http.Json;

var client = new HttpClient();
client.DefaultRequestHeaders.Add("Authorization", "Bearer YOUR_API_KEY");

var response = await client.PostAsJsonAsync("https://aireiter.com/api/v1/responses", new {
  model  = "claude-sonnet-4-5-20250929",
  input  = "Write a one-sentence bedtime story about a unicorn.",
  stream = false
});
Console.WriteLine(await response.Content.ReadAsStringAsync());

#include <stdio.h>
#include <curl/curl.h>

int main() {
  CURL *curl = curl_easy_init();
  struct curl_slist *headers = NULL;
  headers = curl_slist_append(headers, "Authorization: Bearer YOUR_API_KEY");
  headers = curl_slist_append(headers, "Content-Type: application/json");

  const char *data = "{\"model\":\"claude-sonnet-4-5-20250929\","
                     "\"input\":\"Write a one-sentence bedtime story about a unicorn.\","
                     "\"stream\":false}";

  curl_easy_setopt(curl, CURLOPT_URL, "https://aireiter.com/api/v1/responses");
  curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
  curl_easy_setopt(curl, CURLOPT_POSTFIELDS, data);
  curl_easy_perform(curl);
  curl_easy_cleanup(curl);
  return 0;
}

import 'dart:convert';
import 'package:http/http.dart' as http;

void main() async {
  final response = await http.post(
    Uri.parse('https://aireiter.com/api/v1/responses'),
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type':  'application/json',
    },
    body: jsonEncode({
      'model':  'claude-sonnet-4-5-20250929',
      'input':  'Write a one-sentence bedtime story about a unicorn.',
      'stream': false,
    }),
  );
  print(response.body);
}

library(httr)
library(jsonlite)

response <- POST(
  "https://aireiter.com/api/v1/responses",
  add_headers(
    Authorization = "Bearer YOUR_API_KEY",
    `Content-Type` = "application/json"
  ),
  body = toJSON(list(
    model  = "claude-sonnet-4-5-20250929",
    input  = "Write a one-sentence bedtime story about a unicorn.",
    stream = FALSE
  ), auto_unbox = TRUE)
)
content(response, "text")

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0123",
  "object": "response",
  "created_at": 1742000000.123,
  "status": "completed",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_xyz123",
  "output": [
    {
      "id": "msg_AbCdEfGhIjKl0123",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "The little unicorn tiptoed through the moonlit meadow, leaving a trail of shimmering stardust as she galloped home to her cozy cloud.",
          "annotations": []
        }
      ]
    }
  ],
  "output_text": "The little unicorn tiptoed through the moonlit meadow, leaving a trail of shimmering stardust as she galloped home to her cozy cloud.",
  "usage": {
    "input_tokens": 20,
    "output_tokens": 35,
    "total_tokens": 55,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0456",
  "object": "response",
  "created_at": 1742000100.456,
  "status": "completed",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_abc456",
  "output": [
    {
      "id": "fc_AbCdEfGhIjKl0456",
      "type": "function_call",
      "call_id": "call_AbCdEfGh",
      "name": "get_weather",
      "arguments": "{\"location\":\"San Francisco\",\"unit\":\"celsius\"}"
    }
  ],
  "output_text": "",
  "usage": {
    "input_tokens": 80,
    "output_tokens": 25,
    "total_tokens": 105,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0789",
  "object": "response",
  "created_at": 1742000200.789,
  "status": "incomplete",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_def789",
  "output": [
    {
      "id": "msg_AbCdEfGhIjKl0789",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Here is a very long story that was cut off due to max_output_tokens...",
          "annotations": []
        }
      ]
    }
  ],
  "output_text": "Here is a very long story that was cut off due to max_output_tokens...",
  "incomplete_details": {
    "reason": "max_output_tokens"
  },
  "usage": {
    "input_tokens": 15,
    "output_tokens": 256,
    "total_tokens": 271,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "model is required"
  }
}

{
  "type": "error",
  "error": {
    "type": "authentication_error",
    "message": "Invalid API key"
  }
}

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "Insufficient credits"
  }
}

{
  "type": "error",
  "error": {
    "type": "not_found_error",
    "message": "Model 'unknown-model' not found"
  }
}

{
  "type": "error",
  "error": {
    "type": "api_error",
    "message": "All providers failed"
  }
}

POST

api

responses

curl -X POST https://aireiter.com/api/v1/responses \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "input": "Write a one-sentence bedtime story about a unicorn.",
    "stream": true
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://aireiter.com/api/v1"
)

response = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    input="Write a one-sentence bedtime story about a unicorn."
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://aireiter.com/api/v1",
});

const response = await client.responses.create({
  model: "claude-sonnet-4-5-20250929",
  input: "Write a one-sentence bedtime story about a unicorn.",
});

console.log(response.output_text);

package main

import (
  "bytes"
  "encoding/json"
  "fmt"
  "net/http"
)

func main() {
  payload := map[string]interface{}{
    "model":  "claude-sonnet-4-5-20250929",
    "input":  "Write a one-sentence bedtime story about a unicorn.",
    "stream": false,
  }
  body, _ := json.Marshal(payload)

  req, _ := http.NewRequest("POST", "https://aireiter.com/api/v1/responses", bytes.NewBuffer(body))
  req.Header.Set("Authorization", "Bearer YOUR_API_KEY")
  req.Header.Set("Content-Type", "application/json")

  resp, _ := http.DefaultClient.Do(req)
  defer resp.Body.Close()

  var result map[string]interface{}
  json.NewDecoder(resp.Body).Decode(&result)
  fmt.Println(result)
}

import com.fasterxml.jackson.databind.ObjectMapper;
import java.net.URI;
import java.net.http.*;
import java.util.Map;

public class ResponsesExample {
  public static void main(String[] args) throws Exception {
    var payload = Map.of(
      "model", "claude-sonnet-4-5-20250929",
      "input", "Write a one-sentence bedtime story about a unicorn.",
      "stream", false
    );
    var body = new ObjectMapper().writeValueAsString(payload);
    var request = HttpRequest.newBuilder()
      .uri(URI.create("https://aireiter.com/api/v1/responses"))
      .header("Authorization", "Bearer YOUR_API_KEY")
      .header("Content-Type", "application/json")
      .POST(HttpRequest.BodyPublishers.ofString(body))
      .build();
    var response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
    System.out.println(response.body());
  }
}

<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://aireiter.com/api/v1/responses', [
  'headers' => [
    'Authorization' => 'Bearer YOUR_API_KEY',
    'Content-Type'  => 'application/json',
  ],
  'json' => [
    'model'  => 'claude-sonnet-4-5-20250929',
    'input'  => 'Write a one-sentence bedtime story about a unicorn.',
    'stream' => false,
  ],
]);
echo $response->getBody();

require 'net/http'
require 'json'

uri = URI('https://aireiter.com/api/v1/responses')
req = Net::HTTP::Post.new(uri, {
  'Authorization' => 'Bearer YOUR_API_KEY',
  'Content-Type'  => 'application/json'
})
req.body = {
  model:  'claude-sonnet-4-5-20250929',
  input:  'Write a one-sentence bedtime story about a unicorn.',
  stream: false
}.to_json

res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |h| h.request(req) }
puts res.body

import Foundation

let payload: [String: Any] = [
  "model":  "claude-sonnet-4-5-20250929",
  "input":  "Write a one-sentence bedtime story about a unicorn.",
  "stream": false
]

var request = URLRequest(url: URL(string: "https://aireiter.com/api/v1/responses")!)
request.httpMethod = "POST"
request.setValue("Bearer YOUR_API_KEY", forHTTPHeaderField: "Authorization")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try! JSONSerialization.data(withJSONObject: payload)

URLSession.shared.dataTask(with: request) { data, _, _ in
  if let data = data { print(String(data: data, encoding: .utf8)!) }
}.resume()

using System.Net.Http;
using System.Net.Http.Json;

var client = new HttpClient();
client.DefaultRequestHeaders.Add("Authorization", "Bearer YOUR_API_KEY");

var response = await client.PostAsJsonAsync("https://aireiter.com/api/v1/responses", new {
  model  = "claude-sonnet-4-5-20250929",
  input  = "Write a one-sentence bedtime story about a unicorn.",
  stream = false
});
Console.WriteLine(await response.Content.ReadAsStringAsync());

#include <stdio.h>
#include <curl/curl.h>

int main() {
  CURL *curl = curl_easy_init();
  struct curl_slist *headers = NULL;
  headers = curl_slist_append(headers, "Authorization: Bearer YOUR_API_KEY");
  headers = curl_slist_append(headers, "Content-Type: application/json");

  const char *data = "{\"model\":\"claude-sonnet-4-5-20250929\","
                     "\"input\":\"Write a one-sentence bedtime story about a unicorn.\","
                     "\"stream\":false}";

  curl_easy_setopt(curl, CURLOPT_URL, "https://aireiter.com/api/v1/responses");
  curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
  curl_easy_setopt(curl, CURLOPT_POSTFIELDS, data);
  curl_easy_perform(curl);
  curl_easy_cleanup(curl);
  return 0;
}

import 'dart:convert';
import 'package:http/http.dart' as http;

void main() async {
  final response = await http.post(
    Uri.parse('https://aireiter.com/api/v1/responses'),
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type':  'application/json',
    },
    body: jsonEncode({
      'model':  'claude-sonnet-4-5-20250929',
      'input':  'Write a one-sentence bedtime story about a unicorn.',
      'stream': false,
    }),
  );
  print(response.body);
}

library(httr)
library(jsonlite)

response <- POST(
  "https://aireiter.com/api/v1/responses",
  add_headers(
    Authorization = "Bearer YOUR_API_KEY",
    `Content-Type` = "application/json"
  ),
  body = toJSON(list(
    model  = "claude-sonnet-4-5-20250929",
    input  = "Write a one-sentence bedtime story about a unicorn.",
    stream = FALSE
  ), auto_unbox = TRUE)
)
content(response, "text")

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0123",
  "object": "response",
  "created_at": 1742000000.123,
  "status": "completed",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_xyz123",
  "output": [
    {
      "id": "msg_AbCdEfGhIjKl0123",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "The little unicorn tiptoed through the moonlit meadow, leaving a trail of shimmering stardust as she galloped home to her cozy cloud.",
          "annotations": []
        }
      ]
    }
  ],
  "output_text": "The little unicorn tiptoed through the moonlit meadow, leaving a trail of shimmering stardust as she galloped home to her cozy cloud.",
  "usage": {
    "input_tokens": 20,
    "output_tokens": 35,
    "total_tokens": 55,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0456",
  "object": "response",
  "created_at": 1742000100.456,
  "status": "completed",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_abc456",
  "output": [
    {
      "id": "fc_AbCdEfGhIjKl0456",
      "type": "function_call",
      "call_id": "call_AbCdEfGh",
      "name": "get_weather",
      "arguments": "{\"location\":\"San Francisco\",\"unit\":\"celsius\"}"
    }
  ],
  "output_text": "",
  "usage": {
    "input_tokens": 80,
    "output_tokens": 25,
    "total_tokens": 105,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0789",
  "object": "response",
  "created_at": 1742000200.789,
  "status": "incomplete",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_def789",
  "output": [
    {
      "id": "msg_AbCdEfGhIjKl0789",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Here is a very long story that was cut off due to max_output_tokens...",
          "annotations": []
        }
      ]
    }
  ],
  "output_text": "Here is a very long story that was cut off due to max_output_tokens...",
  "incomplete_details": {
    "reason": "max_output_tokens"
  },
  "usage": {
    "input_tokens": 15,
    "output_tokens": 256,
    "total_tokens": 271,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "model is required"
  }
}

{
  "type": "error",
  "error": {
    "type": "authentication_error",
    "message": "Invalid API key"
  }
}

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "Insufficient credits"
  }
}

{
  "type": "error",
  "error": {
    "type": "not_found_error",
    "message": "Model 'unknown-model' not found"
  }
}

{
  "type": "error",
  "error": {
    "type": "api_error",
    "message": "All providers failed"
  }
}

Authorization

string

required

Bearer Token authentication. Format: Authorization: Bearer <api-key>Get API Key:Visit the API Key management page to get your API Key

string

Alternative authentication method, choose either this or Authorization. Format: x-api-key: <api-key>

Request Body Parameters

string

required

The name of the model to use. Examples: gpt-4o, gpt-5.4, gpt-5.5, etc.The full list of models can be obtained via GET /api/v1/models.

array

required

List of input contentsAn array of inputs, each containing the fields role and content. Supports multi-turn conversations and multi-modal content (text + images).

Show Detailed Field Description

string

default:"user"

required

Message roleOptions: user (user message), assistant (AI reply for multi-turn dialogue), system (system prompt)

array

required

Content arraySupports various types of content blocks, including text and images.

Show Content Block Types

string

required

Content typeOptions:

input_text: text input
input_image: image input

string

Text contentUsed when type is input_text; fill in the text content

string

Image URLUsed when type is input_imageSupports two formats:1. Full image URL address

Publicly accessible image URLs (http:// or https://)
Example: https://example.com/image.jpg

2. Base64 encoded format

Must use the full Data URI format
Format: data:image/{format};base64,{base64_data}
Supported image formats: jpeg, png, gif, webp
Example: data:image/jpeg;base64,/9j/4AAQSkZJRgABAQEAYABg...
Note: Must include the prefix data:image/jpeg;base64,

string

System Prompt. Used to set the model’s behavior guidelines, role identity, or context.Equivalent to a message in the array with role: "system".

boolean

default:"true"

Whether to enable streaming output.

true (default): returns tokens incrementally as SSE event stream, suitable for real-time display
false: returns the full response at once after completion, suitable for batch processing

integer

Maximum number of tokens to generate in the reply. If exceeded, the response status will be "incomplete", and incomplete_details.reason will be "max_output_tokens".

number

Sampling temperature, range 0 to 2. Higher values result in more random output, lower values more deterministic. Not recommended to adjust temperature and top_p simultaneously.

number

Nucleus sampling probability, range 0 to 1. The model samples only from tokens whose cumulative probability reaches top_p.

array

List of tools available for the model to call. Currently, only type: "function" is supported; built-in tools like web_search, file_search, computer_use are not supported.

Show Function Tool Format

string

required

Fixed as "function"

string

required

Function name, must match ^[a-zA-Z0-9_-]{1,64}$

string

Function description to help the model decide when to call this tool

object

Parameter definitions, following JSON Schema format

string | object

default:"auto"

Tool calling policy:

"auto": model decides automatically whether to call tools
"none": prohibit calling tools
"required": force calling at least one tool
{ "type": "function", "name": "function_name" }: force call the specified tool

boolean

default:"true"

Whether the model is allowed to call multiple tools in parallel. Only effective if tools are provided.

object

Output format control.

Show Text Object

object

Show Format Object

string

required

Output format type:

"text": plain text (default)
"json_object": JSON format
"json_schema": JSON complying with specified Schema

string

Schema name (effective only for json_schema)

string

Schema description (effective only for json_schema)

boolean

default:"true"

Whether to strictly follow the Schema (effective only for json_schema)

object

JSON Schema definition (required only for json_schema)

object

Reasoning mode configuration (applicable to models supporting reasoning, such as gpt-5.2 and above).

Show Reasoning Object

string

Reasoning intensity: "low" | "medium" | "high"

Response Fields

string

Unique response identifier, format: resp_ + 24-character random string

string

Fixed as "response"

number

Response creation time, Unix timestamp (seconds, with millisecond precision)

string

Response status:

"completed": Completed successfully
"incomplete": Cut off early due to max_output_tokens
"in_progress": Streaming output in progress (only in streaming events)
"failed": Generation failed

string

Actual model name used

string

Platform extension field. Billing task ID for this call, used for reconciliation and consumption queries.Note:

Non-streaming: task_id is located directly at the top level of the response JSON
Streaming: task_id is nested inside the response object of response.created (first event) and response.completed (last event). It needs to be parsed from the raw SSE event. The OpenAI SDK streaming interface does not automatically expose this field

array

Array of output items, which may contain two types: text messages and tool calls.

Show message type

string

Message ID, format: msg_ + 16-character random string

string

Fixed as "message"

string

Fixed as "assistant"

string

"completed" | "in_progress"

array

Array of content blocks:

Show output_text content block

string

Fixed as "output_text"

string

Model-generated text content

array

Text annotations, usually an empty array []

Show function_call type

string

Tool call entry ID, format: fc_ + 16-character random string

string

Fixed as "function_call"

string

Tool call ID, used to reference when submitting tool execution results

string

Name of the called function

string

Function arguments, JSON serialized string

string

Shortcut field, equivalent to output[0].content[0].text (in plain text scenarios). Empty string in tool call scenarios.

object

Token usage statistics.

Show usage object

integer

Number of input tokens (including system prompt)

integer

Number of output tokens

integer

Total tokens = input + output

object

Show Details

integer

Reasoning/thinking tokens (usually 0 for general models)

object | null

Not null when status is "incomplete".

Show incomplete_details object

string

Cutoff reason. Currently only "max_output_tokens"

curl -X POST https://aireiter.com/api/v1/responses \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "claude-sonnet-4-5-20250929",
    "input": "Write a one-sentence bedtime story about a unicorn.",
    "stream": true
  }'

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://aireiter.com/api/v1"
)

response = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    input="Write a one-sentence bedtime story about a unicorn."
)

print(response.output_text)

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: "YOUR_API_KEY",
  baseURL: "https://aireiter.com/api/v1",
});

const response = await client.responses.create({
  model: "claude-sonnet-4-5-20250929",
  input: "Write a one-sentence bedtime story about a unicorn.",
});

console.log(response.output_text);

package main

import (
  "bytes"
  "encoding/json"
  "fmt"
  "net/http"
)

func main() {
  payload := map[string]interface{}{
    "model":  "claude-sonnet-4-5-20250929",
    "input":  "Write a one-sentence bedtime story about a unicorn.",
    "stream": false,
  }
  body, _ := json.Marshal(payload)

  req, _ := http.NewRequest("POST", "https://aireiter.com/api/v1/responses", bytes.NewBuffer(body))
  req.Header.Set("Authorization", "Bearer YOUR_API_KEY")
  req.Header.Set("Content-Type", "application/json")

  resp, _ := http.DefaultClient.Do(req)
  defer resp.Body.Close()

  var result map[string]interface{}
  json.NewDecoder(resp.Body).Decode(&result)
  fmt.Println(result)
}

import com.fasterxml.jackson.databind.ObjectMapper;
import java.net.URI;
import java.net.http.*;
import java.util.Map;

public class ResponsesExample {
  public static void main(String[] args) throws Exception {
    var payload = Map.of(
      "model", "claude-sonnet-4-5-20250929",
      "input", "Write a one-sentence bedtime story about a unicorn.",
      "stream", false
    );
    var body = new ObjectMapper().writeValueAsString(payload);
    var request = HttpRequest.newBuilder()
      .uri(URI.create("https://aireiter.com/api/v1/responses"))
      .header("Authorization", "Bearer YOUR_API_KEY")
      .header("Content-Type", "application/json")
      .POST(HttpRequest.BodyPublishers.ofString(body))
      .build();
    var response = HttpClient.newHttpClient().send(request, HttpResponse.BodyHandlers.ofString());
    System.out.println(response.body());
  }
}

<?php
$client = new GuzzleHttp\Client();
$response = $client->post('https://aireiter.com/api/v1/responses', [
  'headers' => [
    'Authorization' => 'Bearer YOUR_API_KEY',
    'Content-Type'  => 'application/json',
  ],
  'json' => [
    'model'  => 'claude-sonnet-4-5-20250929',
    'input'  => 'Write a one-sentence bedtime story about a unicorn.',
    'stream' => false,
  ],
]);
echo $response->getBody();

require 'net/http'
require 'json'

uri = URI('https://aireiter.com/api/v1/responses')
req = Net::HTTP::Post.new(uri, {
  'Authorization' => 'Bearer YOUR_API_KEY',
  'Content-Type'  => 'application/json'
})
req.body = {
  model:  'claude-sonnet-4-5-20250929',
  input:  'Write a one-sentence bedtime story about a unicorn.',
  stream: false
}.to_json

res = Net::HTTP.start(uri.hostname, uri.port, use_ssl: true) { |h| h.request(req) }
puts res.body

import Foundation

let payload: [String: Any] = [
  "model":  "claude-sonnet-4-5-20250929",
  "input":  "Write a one-sentence bedtime story about a unicorn.",
  "stream": false
]

var request = URLRequest(url: URL(string: "https://aireiter.com/api/v1/responses")!)
request.httpMethod = "POST"
request.setValue("Bearer YOUR_API_KEY", forHTTPHeaderField: "Authorization")
request.setValue("application/json", forHTTPHeaderField: "Content-Type")
request.httpBody = try! JSONSerialization.data(withJSONObject: payload)

URLSession.shared.dataTask(with: request) { data, _, _ in
  if let data = data { print(String(data: data, encoding: .utf8)!) }
}.resume()

using System.Net.Http;
using System.Net.Http.Json;

var client = new HttpClient();
client.DefaultRequestHeaders.Add("Authorization", "Bearer YOUR_API_KEY");

var response = await client.PostAsJsonAsync("https://aireiter.com/api/v1/responses", new {
  model  = "claude-sonnet-4-5-20250929",
  input  = "Write a one-sentence bedtime story about a unicorn.",
  stream = false
});
Console.WriteLine(await response.Content.ReadAsStringAsync());

#include <stdio.h>
#include <curl/curl.h>

int main() {
  CURL *curl = curl_easy_init();
  struct curl_slist *headers = NULL;
  headers = curl_slist_append(headers, "Authorization: Bearer YOUR_API_KEY");
  headers = curl_slist_append(headers, "Content-Type: application/json");

  const char *data = "{\"model\":\"claude-sonnet-4-5-20250929\","
                     "\"input\":\"Write a one-sentence bedtime story about a unicorn.\","
                     "\"stream\":false}";

  curl_easy_setopt(curl, CURLOPT_URL, "https://aireiter.com/api/v1/responses");
  curl_easy_setopt(curl, CURLOPT_HTTPHEADER, headers);
  curl_easy_setopt(curl, CURLOPT_POSTFIELDS, data);
  curl_easy_perform(curl);
  curl_easy_cleanup(curl);
  return 0;
}

import 'dart:convert';
import 'package:http/http.dart' as http;

void main() async {
  final response = await http.post(
    Uri.parse('https://aireiter.com/api/v1/responses'),
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY',
      'Content-Type':  'application/json',
    },
    body: jsonEncode({
      'model':  'claude-sonnet-4-5-20250929',
      'input':  'Write a one-sentence bedtime story about a unicorn.',
      'stream': false,
    }),
  );
  print(response.body);
}

library(httr)
library(jsonlite)

response <- POST(
  "https://aireiter.com/api/v1/responses",
  add_headers(
    Authorization = "Bearer YOUR_API_KEY",
    `Content-Type` = "application/json"
  ),
  body = toJSON(list(
    model  = "claude-sonnet-4-5-20250929",
    input  = "Write a one-sentence bedtime story about a unicorn.",
    stream = FALSE
  ), auto_unbox = TRUE)
)
content(response, "text")

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0123",
  "object": "response",
  "created_at": 1742000000.123,
  "status": "completed",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_xyz123",
  "output": [
    {
      "id": "msg_AbCdEfGhIjKl0123",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "The little unicorn tiptoed through the moonlit meadow, leaving a trail of shimmering stardust as she galloped home to her cozy cloud.",
          "annotations": []
        }
      ]
    }
  ],
  "output_text": "The little unicorn tiptoed through the moonlit meadow, leaving a trail of shimmering stardust as she galloped home to her cozy cloud.",
  "usage": {
    "input_tokens": 20,
    "output_tokens": 35,
    "total_tokens": 55,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0456",
  "object": "response",
  "created_at": 1742000100.456,
  "status": "completed",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_abc456",
  "output": [
    {
      "id": "fc_AbCdEfGhIjKl0456",
      "type": "function_call",
      "call_id": "call_AbCdEfGh",
      "name": "get_weather",
      "arguments": "{\"location\":\"San Francisco\",\"unit\":\"celsius\"}"
    }
  ],
  "output_text": "",
  "usage": {
    "input_tokens": 80,
    "output_tokens": 25,
    "total_tokens": 105,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "id": "resp_AbCdEfGhIjKlMnOpQrSt0789",
  "object": "response",
  "created_at": 1742000200.789,
  "status": "incomplete",
  "model": "claude-sonnet-4-5-20250929",
  "task_id": "task_def789",
  "output": [
    {
      "id": "msg_AbCdEfGhIjKl0789",
      "type": "message",
      "role": "assistant",
      "status": "completed",
      "content": [
        {
          "type": "output_text",
          "text": "Here is a very long story that was cut off due to max_output_tokens...",
          "annotations": []
        }
      ]
    }
  ],
  "output_text": "Here is a very long story that was cut off due to max_output_tokens...",
  "incomplete_details": {
    "reason": "max_output_tokens"
  },
  "usage": {
    "input_tokens": 15,
    "output_tokens": 256,
    "total_tokens": 271,
    "output_tokens_details": {
      "reasoning_tokens": 0
    }
  }
}

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "model is required"
  }
}

{
  "type": "error",
  "error": {
    "type": "authentication_error",
    "message": "Invalid API key"
  }
}

{
  "type": "error",
  "error": {
    "type": "invalid_request_error",
    "message": "Insufficient credits"
  }
}

{
  "type": "error",
  "error": {
    "type": "not_found_error",
    "message": "Model 'unknown-model' not found"
  }
}

{
  "type": "error",
  "error": {
    "type": "api_error",
    "message": "All providers failed"
  }
}

Streaming Response Events

When stream: true, the API returns an SSE event stream in text/event-stream format. Each event has the following format:

event: <event_type>
data: <JSON_payload>

All event payloads include a sequence_number field (incrementing from 0) to ensure clients process events in order.

Obtaining task_id: To get the billing task ID in streaming mode, listen for the first response.created event and read event.response.task_id.

Event Sequence (Text Response)

No.	Event Type	Description
1	`response.created`	Response object created, `status: "in_progress"`
2	`response.in_progress`	Response generation started
3	`response.output_item.added`	Output message item added
4	`response.content_part.added`	Text content chunk added, `text: ""`
5	`response.output_text.delta`	(Repeated) Incremental text per token
6	`response.output_text.done`	Text content completed with full text
7	`response.content_part.done`	Content chunk completed
8	`response.output_item.done`	Message item completed
9	`response.completed`	Response completed with full response object and usage

Event Sequence (Tool Invocation)

No.	Event Type	Description
…	`response.output_item.added`	Function call item added
…	`response.function_call_arguments.delta`	(Repeated) Function arguments incremental
…	`response.function_call_arguments.done`	Function arguments completed
…	`response.output_item.done`	Function call item completed
Last	`response.completed`	Response completed

Event Examples

{
  "type": "response.created",
  "sequence_number": 0,
  "response": {
    "id": "resp_AbCdEfGhIjKlMnOpQrSt0123",
    "object": "response",
    "created_at": 1742000000.123,
    "status": "in_progress",
    "model": "claude-sonnet-4-5-20250929",
    "task_id": "task_xyz123",
    "output": [],
    "output_text": ""
  }
}

{
  "type": "response.output_text.delta",
  "sequence_number": 5,
  "item_id": "msg_AbCdEfGhIjKl0123",
  "output_index": 0,
  "content_index": 0,
  "delta": "The little"
}

{
  "type": "response.completed",
  "sequence_number": 9,
  "response": {
    "id": "resp_AbCdEfGhIjKlMnOpQrSt0123",
    "object": "response",
    "created_at": 1742000000.123,
    "status": "completed",
    "model": "claude-sonnet-4-5-20250929",
    "task_id": "task_xyz123",
    "output": [
      {
        "id": "msg_AbCdEfGhIjKl0123",
        "type": "message",
        "role": "assistant",
        "status": "completed",
        "content": [
          {
            "type": "output_text",
            "text": "The little unicorn tiptoed through the moonlit meadow.",
            "annotations": []
          }
        ]
      }
    ],
    "output_text": "The little unicorn tiptoed through the moonlit meadow.",
    "usage": {
      "input_tokens": 20,
      "output_tokens": 12,
      "total_tokens": 32,
      "output_tokens_details": { "reasoning_tokens": 0 }
    }
  }
}

{
  "type": "response.function_call_arguments.delta",
  "sequence_number": 7,
  "output_index": 0,
  "delta": "{\"location\":"
}

Usage Examples

Basic Text Conversation

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://aireiter.com/api/v1"
)

response = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    instructions="You are a helpful coding assistant.",
    input="How do I reverse a string in Python?",
)

print(response.output_text)

Image Understanding

response = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    input=[
        {
            "role": "user",
            "content": [
                {"type": "input_text",  "text": "What's in this image?"},
                {"type": "input_image", "image_url": {"url": "https://example.com/photo.jpg"}},
            ],
        }
    ],
)

print(response.output_text)

Tool Invocation

tools = [
    {
        "type": "function",
        "name": "get_weather",
        "description": "Get current weather for a location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {"type": "string", "description": "City name"},
                "unit":     {"type": "string", "enum": ["celsius", "fahrenheit"]},
            },
            "required": ["location"],
        },
    }
]

response = client.responses.create(
    model="gpt-5.2",
    input="What's the weather like in Tokyo?",
    tools=tools,
    tool_choice="auto",
)

# Check for tool calls
for item in response.output:
    if item.type == "function_call":
        print(f"Tool: {item.name}, Args: {item.arguments}")

Structured Output (JSON Schema)

import json

schema = {
    "type": "object",
    "properties": {
        "name":    {"type": "string"},
        "age":     {"type": "integer"},
        "hobbies": {"type": "array", "items": {"type": "string"}},
    },
    "required": ["name", "age", "hobbies"],
    "additionalProperties": False,
}

response = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    input="Extract info: Alice is 30 years old and loves hiking and cooking.",
    text={
        "format": {
            "type":   "json_schema",
            "name":   "person_info",
            "strict": True,
            "schema": schema,
        }
    },
)

data = json.loads(response.output_text)
print(data)  # {"name": "Alice", "age": 30, "hobbies": ["hiking", "cooking"]}

Reasoning Mode

response = client.responses.create(
    model="claude-3-7-sonnet-20250219",  # Model supporting reasoning
    input="Solve: If x + y = 10 and x * y = 21, what are x and y?",
    reasoning={"effort": "high"},
)

print(response.output_text)

Multi-turn Conversation

previous_response_id is currently ineffective; you need to manually concatenate historical messages in input:

# First turn
response1 = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    input="My name is Bob.",
)

# Second turn: manually concatenate history
response2 = client.responses.create(
    model="claude-sonnet-4-5-20250929",
    input=[
        {"role": "user",      "content": "My name is Bob."},
        {"role": "assistant", "content": response1.output_text},
        {"role": "user",      "content": "What's my name?"},
    ],
)

print(response2.output_text)  # "Your name is Bob."

Notes

Streaming by Default: The stream parameter defaults to true. To receive a non-streaming response, explicitly pass "stream": false.
Insufficient Credits: An HTTP 402 is returned when the balance is insufficient. Please recharge and try again.
Limited Support for text.format: The underlying provider currently has limited support for structured output—models may still output Markdown code blocks instead of pure JSON in json_object mode; Schema constraints may not be enforced in json_schema mode. For structured output, it is recommended to clearly describe the required format in instructions or input.
Tool Parameters: The parameters field must be a valid JSON Schema, and the required array specifies which parameters are mandatory.

Error Code Description

HTTP Status Code	error.type	Description
400	`invalid_request_error`	Invalid request parameters (missing required fields, JSON parsing failure, etc.)
401	`authentication_error`	API Key is invalid or expired
402	`invalid_request_error`	Insufficient account balance
404	`not_found_error`	Specified model does not exist
502	`api_error`	All upstream AI providers are unavailable
503	`api_error`	No available provider configuration

Overview

Image Series

Video Series

Audio Series

Text Series

Task Management

OpenAI Responses API

Authorization

Request Body Parameters

Response Fields

Streaming Response Events

Event Sequence (Text Response)

Event Sequence (Tool Invocation)

Event Examples

Usage Examples

Basic Text Conversation

Image Understanding

Tool Invocation

Structured Output (JSON Schema)

Reasoning Mode

Multi-turn Conversation

Notes

Error Code Description

​Authorization

​Request Body Parameters

​Response Fields

​Streaming Response Events

​Event Sequence (Text Response)

​Event Sequence (Tool Invocation)

​Event Examples

​Usage Examples

​Basic Text Conversation

​Image Understanding

​Tool Invocation

​Structured Output (JSON Schema)

​Reasoning Mode

​Multi-turn Conversation

​Notes

​Error Code Description

Authorization

Request Body Parameters

Response Fields

Streaming Response Events

Event Sequence (Text Response)

Event Sequence (Tool Invocation)

Event Examples

Usage Examples

Basic Text Conversation

Image Understanding

Tool Invocation

Structured Output (JSON Schema)

Reasoning Mode

Multi-turn Conversation

Notes

Error Code Description