Last Update: 2025/3/26

OpenAI Completion API

The OpenAI Completion API allows you to generate text completions using OpenAI's language models. This document provides an overview of the API endpoints, request parameters, and response structure.

Endpoint

POST https://platform.llmprovider.ai/v1/completions

Request Headers

Header	Value
Authorization	Bearer YOUR_API_KEY
Content-Type	application/json

Request Body

The request body should be a JSON object with the following parameters:

Parameter	Type	Description
model	string	The model to use (e.g., gpt-3.5-turbo-instruct).
prompt	string	The prompt to generate completions for.
max_tokens	integer	(Optional) The maximum number of tokens that can be generated in the completion.
temperature	number	(Optional) Sampling temperature, between 0 and 2.
top_p	number	(Optional) Nucleus sampling probability, between 0 and 1.
n	integer	(Optional) Number of completions to generate for each prompt.
stop	array	(Optional) Up to 4 sequences where the API will stop generating further tokens.
presence_penalty	number	(Optional) Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far.
frequency_penalty	number	(Optional) Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far.

Example Request

{
  "model": "gpt-3.5-turbo-instruct",
  "prompt": "Say this is a test",
  "max_tokens": 7,
  "temperature": 0
}

Response Body

The response body will be a JSON object containing the generated completions and other metadata.

Field	Type	Description
id	string	Unique identifier for the completion.
object	string	The type of object returned, usually text_completion.
created	integer	Timestamp of when the completion was created.
model	string	The model used for the completion.
choices	array	A list of generated completion choices.
choices[].finish_reason	string	The reason why the completion ended (e.g., stop, length).
choices[].index	integer	The index of the choice in the returned list.
choices[].logprobs	object	(Optional) Log probabilities of the tokens in the completion.
choices[].text	string	The generated text for the completion.
usage	object	Token usage statistics for the request.

Example Response

{
  "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
  "object": "text_completion",
  "created": 1589478378,
  "model": "gpt-3.5-turbo-instruct",
  "choices": [
    {
      "text": "\n\nThis is indeed a test",
      "index": 0,
      "logprobs": null,
      "finish_reason": "length"
    }
  ],
  "usage": {
    "prompt_tokens": 5,
    "completion_tokens": 7,
    "total_tokens": 12
  }
}

Example Requests

Shell

curl -X POST https://platform.llmprovider.ai/v1/completions \
-H "Authorization: Bearer $YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-3.5-turbo-instruct",
"prompt": "Hello, world!",
"max_tokens": 50
}'

Node.js

const axios = require('axios');
const apiKey = 'YOUR_API_KEY';
const url = 'https://platform.llmprovider.ai/v1/completions';

const data = {
    model: 'gpt-3.5-turbo-instruct',
    prompt: 'Hello, world!',
    max_tokens: 50
};

const headers = {
    'Authorization': `Bearer ${apiKey}`,
    'Content-Type': 'application/json'
};

axios.post(url, data, {headers})
    .then(response => {
        console.log('Response:', response.data);
    })
    .catch(error => {
        console.error('Error:', error);
    });

Python

import requests
import json

api_key = 'YOUR_API_KEY'
url = 'https://platform.llmprovider.ai/v1/completions'

headers = {
    'Authorization': f'Bearer {api_key}',
    'Content-Type': 'application/json'
}

data = {
    'model': 'gpt-3.5-turbo-instruct',
    'prompt': 'Hello, world!',
    'max_tokens': 50
}

response = requests.post(url, headers=headers, data=json.dumps(data))

if response.status_code == 200:
    print('Response:', response.json())
else:
    print('Error:', response.status_code, response.text)

For more details, refer to the OpenAI API documentation.