Completions

Given a prompt, the model will return one or more predicted completions, and can also return the probabilities of alternative tokens at each position.

Create completion

POST https://api.openai.com/v1/completions

Creates a completion for the provided prompt and parameters

Request body

---

modelstring Required

ID of the model to use. You can use the List models API to see all of your available models, or see our Model overview for descriptions of them.

---

promptstring or arrayOptionalDefaults to <|endoftext|>

The prompt(s) to generate completions for, encoded as a string, array of strings, array of tokens, or array of token arrays.

---

suffixstringOptionalDefaults to null

The suffix that comes after a completion of inserted text.

---

max_tokensintegerOptionalDefaults to 16

The maximum number of tokens to generate in the completion.

The token count of your prompt plus max_tokens cannot exceed the model's context length. Most models have a context length of 2048 tokens (except for the newest models, which support 4096).

---

temperaturenumberOptionalDefaults to 1

What sampling temperature to use, between 0 and 2. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic.

We generally recommend altering this or top_p but not both.

---

top_pnumberOptionalDefaults to 1

An alternative to sampling with temperature, called nucleus sampling, where the model considers the results of the tokens with top_p probability mass. So 0.1 means only the tokens comprising the top 10% probability mass are considered.

We generally recommend altering this or temperature but not both.

---

nintegerOptionalDefaults to 1

How many completions to generate for each prompt.

---

streambooleanOptionalDefaults to false

Whether to stream back partial progress. If set, tokens will be sent as data-only server-sent events as they become available, with the stream terminated by a data: [DONE] message.

---

logprobsintegerOptionalDefaults to null

Include the log probabilities on the logprobs most likely tokens, as well the chosen tokens. For example, if logprobs is 5, the API will return a list of the 5 most likely tokens. The API will always return the logprob of the sampled token, so there may be up to logprobs+1 elements in the response.

The maximum value for logprobs is 5. If you need more than this, please contact us through our Help center and describe your use case.

---

echobooleanOptionalDefaults to false

Echo back the prompt in addition to the completion

---

stopstring or arrayOptionalDefaults to null

Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.

---

presence_penaltynumberOptionalDefaults to 0

Number between -2.0 and 2.0. Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood to talk about new topics.

See more information about frequency and presence penalties.

---

frequency_penaltynumberOptionalDefaults to 0

Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.

See more information about frequency and presence penalties.

---

best_ofintegerOptionalDefaults to 1

Generates best_of completions server-side and returns the "best" (the one with the highest log probability per token). Results cannot be streamed.

When used with n, best_of controls the number of candidate completions and n specifies how many to return – best_of must be greater than n.

---

logit_biasmapOptionalDefaults to null

Modify the likelihood of specified tokens appearing in the completion.

Accepts a json object that maps tokens (specified by their token ID in the GPT tokenizer) to an associated bias value from -100 to 100. You can use this tokenizer tool (which works for both GPT-2 and GPT-3) to convert text to token IDs. Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model, but values between -1 and 1 should decrease or increase likelihood of selection; values like -100 or 100 should result in a ban or exclusive selection of the relevant token.

As an example, you can pass {"50256": -100} to prevent the <|endoftext|> token from being generated.

---

userstringOptional

A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. Learn more.

---

Example request:

curl:

curl https://api.openai.com/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $OPENAI_API_KEY" \
  -d '{
    "model": "babbage",
    "prompt": "Say this is a test",
    "max_tokens": 7,
    "temperature": 0
  }'

python:

import os
import openai
openai.api_key = os.getenv("OPENAI_API_KEY")
openai.Completion.create(
  model="babbage",
  prompt="Say this is a test",
  max_tokens=7,
  temperature=0
)

node.js:

const { Configuration, OpenAIApi } = require("openai");
const configuration = new Configuration({
  apiKey: process.env.OPENAI_API_KEY,
});
const openai = new OpenAIApi(configuration);
const response = await openai.createCompletion({
  model: "babbage",
  prompt: "Say this is a test",
  max_tokens: 7,
  temperature: 0,
});

Parameters:

{
  "model": "babbage",
  "prompt": "Say this is a test",
  "max_tokens": 7,
  "temperature": 0,
  "top_p": 1,
  "n": 1,
  "stream": false,
  "logprobs": null,
  "stop": "\n"
}

Response:

{
  "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
  "object": "text_completion",
  "created": 1589478378,
  "model": "babbage",
  "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
  }
}

Supported models:

• babbage
• davinci
• text-davinci-001
• ada
• text-curie-001
• text-davinci-003
• text-ada-001
• curie-instruct-beta
• davinci-instruct-beta
• text-babbage-001
• curie
• text-davinci-002