---
title: "Creates a completion for the provided prompt and parameters."
method: POST
path: "/completions"
tags: ["Completions"]
---

# Creates a completion for the provided prompt and parameters.

`POST /completions`

## Request body

- CreateCompletionRequest
  - `model` union, required — ID of the model to use. You can use the [List models](/docs/api-reference/models/list) API to see all of your available models, or see our [Model overview](/docs/models/overview) for descriptions of them.
    - string
    - 'gpt-3.5-turbo-instruct' | 'davinci-002' | 'babbage-002'
  - `prompt` union, required — 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
    - string[]
    - integer[]
    - array[]
      - integer[]
  - `best_of` integer, nullable — 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`.
  - `echo` boolean, nullable — Echo back the prompt in addition to the completion
  - `frequency_penalty` number, nullable — 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.](/docs/guides/text-generation/parameter-details)
  - `logit_bias` object, nullable — 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](/tokenizer?view=bpe) 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.
  - `logprobs` integer, nullable — 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.
  - `max_tokens` integer, nullable — The maximum number of [tokens](/tokenizer) 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](https://cookbook.openai.com/examples/how_to_count_tokens_with_tiktoken) for counting tokens.
  - `n` integer, nullable — 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`.
  - `presence_penalty` number, nullable — 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.](/docs/guides/text-generation/parameter-details)
  - `seed` integer, nullable — 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.
  - `stop` union — Up to 4 sequences where the API will stop generating further tokens. The returned text will not contain the stop sequence.
    - string, nullable
    - string[]
  - `stream` boolean, nullable — Whether to stream back partial progress. If set, tokens will be sent as data-only [server-sent events](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events#Event_stream_format) as they become available, with the stream terminated by a `data: [DONE]` message. [Example Python code](https://cookbook.openai.com/examples/how_to_stream_completions).
  - `stream_options` ChatCompletionStreamOptions, nullable — Options for streaming response. Only set this when you set `stream: true`.
    - `include_usage` boolean — If set, an additional chunk will be streamed before the `data: [DONE]` message. The `usage` field on this chunk shows the token usage statistics for the entire request, and the `choices` field will always be an empty array. All other chunks will also include a `usage` field, but with a null value.
  - `suffix` string, nullable — The suffix that comes after a completion of inserted text. This parameter is only supported for `gpt-3.5-turbo-instruct`.
  - `temperature` number, nullable — 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_p` number, nullable — 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.
  - `user` string — A unique identifier representing your end-user, which can help OpenAI to monitor and detect abuse. [Learn more](/docs/guides/safety-best-practices/end-user-ids).

## Response `200`

OK

- CreateCompletionResponse — Represents a completion response from the API. Note: both the streamed and non-streamed response objects share the same shape (unlike the chat endpoint).
  - `id` string, required — A unique identifier for the completion.
  - `choices` object[], required — The list of completion choices the model generated for the input prompt.
    - `finish_reason` 'stop' | 'length' | 'content_filter', required — 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.
    - `index` integer, required
    - `logprobs` object, nullable, required
      - `text_offset` integer[]
      - `token_logprobs` number[]
      - `tokens` string[]
      - `top_logprobs` object[]
    - `text` string, required
  - `created` integer, required — The Unix timestamp (in seconds) of when the completion was created.
  - `model` string, required — The model used for completion.
  - `system_fingerprint` string — 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.
  - `object` 'text_completion', required — The object type, which is always "text_completion"
  - `usage` CompletionUsage — Usage statistics for the completion request.
    - `completion_tokens` integer, required — Number of tokens in the generated completion.
    - `prompt_tokens` integer, required — Number of tokens in the prompt.
    - `total_tokens` integer, required — Total number of tokens used in the request (prompt + completion).
    - `completion_tokens_details` object — Breakdown of tokens used in a completion.
      - `reasoning_tokens` integer — Tokens generated by the model for reasoning.

---

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