API Reference

Completions

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Create completion

Completion completions().create(CompletionCreateParamsparams, RequestOptionsrequestOptions = RequestOptions.none())

POST/completions

Creates a completion for the provided prompt and parameters.

ParametersExpand Collapse

CompletionCreateParams params

Model model

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.

GPT_3_5_TURBO_INSTRUCT("gpt-3.5-turbo-instruct")

DAVINCI_002("davinci-002")

BABBAGE_002("babbage-002")

Optional<Prompt> prompt

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

Note that <|endoftext|> is the document separator that the model sees during training, so if a prompt is not specified the model will generate as if from the beginning of a new document.

String

List<String>

List<long>

List<List<long>>

Optional<Long> bestOf

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.

Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

minimum0

maximum20

Optional<Boolean> echo

Echo back the prompt in addition to the completion

Optional<Double> frequencyPenalty

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.

minimum-2

maximum2

Optional<LogitBias> logitBias

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 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.

Optional<Long> logprobs

Include the log probabilities on the logprobs most likely output 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.

minimum0

maximum5

Optional<Long> maxTokens

The maximum number of tokens that can be generated in the completion.

The token count of your prompt plus max_tokens cannot exceed the model's context length. Example Python code for counting tokens.

minimum0

Optional<Long> n

How many completions to generate for each prompt.

Note: Because this parameter generates many completions, it can quickly consume your token quota. Use carefully and ensure that you have reasonable settings for max_tokens and stop.

minimum1

maximum128

Optional<Double> presencePenalty

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.

minimum-2

maximum2

Optional<Long> seed

If specified, our system will make a best effort to sample deterministically, such that repeated requests with the same seed and parameters should return the same result.

Determinism is not guaranteed, and you should refer to the system_fingerprint response parameter to monitor changes in the backend.

formatint64

Optional<Stop> stop

Not supported with latest reasoning models o3 and o4-mini.

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

String

List<String>

Optional<ChatCompletionStreamOptions> streamOptions

Options for streaming response. Only set this when you set stream: true.

Optional<String> suffix

The suffix that comes after a completion of inserted text.

This parameter is only supported for gpt-3.5-turbo-instruct.

Optional<Double> temperature

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.

minimum0

maximum2

Optional<Double> topP

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.

minimum0

maximum1

Optional<String> user

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

ReturnsExpand Collapse

class Completion:

Represents a completion response from the API. Note: both the streamed and non-streamed response objects share the same shape (unlike the chat endpoint).

String id

A unique identifier for the completion.

List<CompletionChoice> choices

The list of completion choices the model generated for the input prompt.

FinishReason finishReason

The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, or content_filter if content was omitted due to a flag from our content filters.

Accepts one of the following:

STOP("stop")

LENGTH("length")

CONTENT_FILTER("content_filter")

long index

Optional<Logprobs> logprobs

Optional<List<Long>> textOffset

Optional<List<Double>> tokenLogprobs

Optional<List<String>> tokens

Optional<List<TopLogprob>> topLogprobs

String text

long created

The Unix timestamp (in seconds) of when the completion was created.

String model

The model used for completion.

JsonValue; object_ "text_completion"constant"text_completion"constant

The object type, which is always "text_completion"

Optional<String> systemFingerprint

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

Optional<CompletionUsage> usage

Usage statistics for the completion request.

long completionTokens

Number of tokens in the generated completion.

long promptTokens

Number of tokens in the prompt.

long totalTokens

Total number of tokens used in the request (prompt + completion).

Optional<CompletionTokensDetails> completionTokensDetails

Breakdown of tokens used in a completion.

Optional<Long> acceptedPredictionTokens

When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.

Optional<Long> audioTokens

Audio input tokens generated by the model.

Optional<Long> reasoningTokens

Tokens generated by the model for reasoning.

Optional<Long> rejectedPredictionTokens

When using Predicted Outputs, the number of tokens in the prediction that did not appear in the completion. However, like reasoning tokens, these tokens are still counted in the total completion tokens for purposes of billing, output, and context window limits.

Optional<PromptTokensDetails> promptTokensDetails

Breakdown of tokens used in the prompt.

Optional<Long> audioTokens

Audio input tokens present in the prompt.

Optional<Long> cachedTokens

Cached tokens present in the prompt.

class Completion:

Represents a completion response from the API. Note: both the streamed and non-streamed response objects share the same shape (unlike the chat endpoint).

String id

A unique identifier for the completion.

List<CompletionChoice> choices

The list of completion choices the model generated for the input prompt.

FinishReason finishReason

The reason the model stopped generating tokens. This will be stop if the model hit a natural stop point or a provided stop sequence, length if the maximum number of tokens specified in the request was reached, or content_filter if content was omitted due to a flag from our content filters.

Accepts one of the following:

STOP("stop")

LENGTH("length")

CONTENT_FILTER("content_filter")

long index

Optional<Logprobs> logprobs

Optional<List<Long>> textOffset

Optional<List<Double>> tokenLogprobs

Optional<List<String>> tokens

Optional<List<TopLogprob>> topLogprobs

String text

long created

The Unix timestamp (in seconds) of when the completion was created.

String model

The model used for completion.

JsonValue; object_ "text_completion"constant"text_completion"constant

The object type, which is always "text_completion"

Optional<String> systemFingerprint

This fingerprint represents the backend configuration that the model runs with.

Can be used in conjunction with the seed request parameter to understand when backend changes have been made that might impact determinism.

Optional<CompletionUsage> usage

Usage statistics for the completion request.

long completionTokens

Number of tokens in the generated completion.

long promptTokens

Number of tokens in the prompt.

long totalTokens

Total number of tokens used in the request (prompt + completion).

Optional<CompletionTokensDetails> completionTokensDetails

Breakdown of tokens used in a completion.

Optional<Long> acceptedPredictionTokens

When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.

Optional<Long> audioTokens

Audio input tokens generated by the model.

Optional<Long> reasoningTokens

Tokens generated by the model for reasoning.

Optional<Long> rejectedPredictionTokens

When using Predicted Outputs, the number of tokens in the prediction that did not appear in the completion. However, like reasoning tokens, these tokens are still counted in the total completion tokens for purposes of billing, output, and context window limits.

Optional<PromptTokensDetails> promptTokensDetails

Breakdown of tokens used in the prompt.

Optional<Long> audioTokens

Audio input tokens present in the prompt.

Optional<Long> cachedTokens

Cached tokens present in the prompt.

Create completion

Java

HTTP

TypeScript

Python

Java

Go

Ruby

package com.openai.example;

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.completions.Completion;
import com.openai.models.completions.CompletionCreateParams;

public final class Main {
    private Main() {}

    public static void main(String[] args) {
        OpenAIClient client = OpenAIOkHttpClient.fromEnv();

        CompletionCreateParams params = CompletionCreateParams.builder()
            .model(CompletionCreateParams.Model.GPT_3_5_TURBO_INSTRUCT)
            .prompt("This is a test.")
            .build();
        Completion completion = client.completions().create(params);
    }
}

200 example

{
  "id": "id",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": {
        "text_offset": [
          0
        ],
        "token_logprobs": [
          0
        ],
        "tokens": [
          "string"
        ],
        "top_logprobs": [
          {
            "foo": 0
          }
        ]
      },
      "text": "text"
    }
  ],
  "created": 0,
  "model": "model",
  "object": "text_completion",
  "system_fingerprint": "system_fingerprint",
  "usage": {
    "completion_tokens": 0,
    "prompt_tokens": 0,
    "total_tokens": 0,
    "completion_tokens_details": {
      "accepted_prediction_tokens": 0,
      "audio_tokens": 0,
      "reasoning_tokens": 0,
      "rejected_prediction_tokens": 0
    },
    "prompt_tokens_details": {
      "audio_tokens": 0,
      "cached_tokens": 0
    }
  }
}

Returns Examples

200 example

{
  "id": "id",
  "choices": [
    {
      "finish_reason": "stop",
      "index": 0,
      "logprobs": {
        "text_offset": [
          0
        ],
        "token_logprobs": [
          0
        ],
        "tokens": [
          "string"
        ],
        "top_logprobs": [
          {
            "foo": 0
          }
        ]
      },
      "text": "text"
    }
  ],
  "created": 0,
  "model": "model",
  "object": "text_completion",
  "system_fingerprint": "system_fingerprint",
  "usage": {
    "completion_tokens": 0,
    "prompt_tokens": 0,
    "total_tokens": 0,
    "completion_tokens_details": {
      "accepted_prediction_tokens": 0,
      "audio_tokens": 0,
      "reasoning_tokens": 0,
      "rejected_prediction_tokens": 0
    },
    "prompt_tokens_details": {
      "audio_tokens": 0,
      "cached_tokens": 0
    }
  }
}