Completions

Create completion

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

post /completions

Creates a completion for the provided prompt and parameters.

Returns a completion object, or a sequence of completion objects if the request is streamed.

Parameters

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

Returns

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.
    - 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
  
  The object type, which is always "text_completion"
  - TEXT_COMPLETION("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> cacheWriteTokens
      
      The unadjusted number of prompt tokens written to cache.
    - Optional<Long> cachedTokens
      
      Cached tokens present in the prompt.

Example

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);
    }
}

Response

{
  "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,
      "cache_write_tokens": 0,
      "cached_tokens": 0
    }
  }
}

Domain Types

Completion

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.
    - 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
  
  The object type, which is always "text_completion"
  - TEXT_COMPLETION("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> cacheWriteTokens
      
      The unadjusted number of prompt tokens written to cache.
    - Optional<Long> cachedTokens
      
      Cached tokens present in the prompt.

Completion Choice

class CompletionChoice:
- 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.
  - 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

Completion Usage

class CompletionUsage:

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> cacheWriteTokens
    
    The unadjusted number of prompt tokens written to cache.
  - Optional<Long> cachedTokens
    
    Cached tokens present in the prompt.

java/resources/completions/index.md 2026-07-10 23:02 UTC to 2026-07-12 06:58 UTC