---
title: "Completion"
method: POST
path: "/v2/completions"
---

# Completion

`POST /v2/completions`

## Request body

- CompletionV2Request
  - `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`.
  - `top_k` integer, nullable — Controls the number of top tokens to consider. -1 means consider all tokens.
  - `min_p` number, nullable — Float that represents the minimum probability for a token to be considered, relative to the probability of the most likely token. Must be in [0, 1]. Set to 0 to disable this.
  - `use_beam_search` boolean, nullable — Whether to use beam search for sampling.
  - `length_penalty` number, nullable — Float that penalizes sequences based on their length. Used in beam search.
  - `repetition_penalty` number, nullable — Float that penalizes new tokens based on whether they appear in the prompt and the generated text so far. Values > 1 encourage the model to use new tokens, while values < 1 encourage the model to repeat tokens.
  - `early_stopping` boolean, nullable — Controls the stopping condition for beam search. It accepts the following values: `True`, where the generation stops as soon as there are `best_of` complete candidates; `False`, where an heuristic is applied and the generation stops when is it very unlikely to find better candidates; `"never"`, where the beam search procedure only stops when there cannot be better candidates (canonical beam search algorithm).
  - `stop_token_ids` integer[], nullable — List of tokens that stop the generation when they are generated. The returned output will contain the stop tokens unless the stop tokens are special tokens.
  - `include_stop_str_in_output` boolean, nullable — Whether to include the stop strings in output text.
  - `ignore_eos` boolean, nullable — Whether to ignore the EOS token and continue generating tokens after the EOS token is generated.
  - `min_tokens` integer, nullable — Minimum number of tokens to generate per output sequence before EOS or stop_token_ids can be generated
  - `skip_special_tokens` boolean, nullable — Whether to skip special tokens in the output. Only supported in vllm.
  - `spaces_between_special_tokens` boolean, nullable — Whether to add spaces between special tokens in the output. Only supported in vllm.
  - `add_special_tokens` boolean, nullable — If true (the default), special tokens (e.g. BOS) will be added to the prompt.
  - `response_format` union — Similar to chat completion, this parameter specifies the format of output. Only {'type': 'json_object'} or {'type': 'text' } is supported.
    - ResponseFormatText
      - `type` 'text', required — The type of response format being defined. Always `text`.
    - ResponseFormatJsonSchema
      - `type` 'json_schema', required — The type of response format being defined. Always `json_schema`.
      - `json_schema` JsonSchema, required
        - `description` string, nullable — A description of what the response format is for, used by the model to determine how to respond in the format.
        - `name` string, required — The name of the response format. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
        - `schema` ResponseFormatJsonSchemaSchema
        - `strict` boolean, nullable — Whether to enable strict schema adherence when generating the output. If set to true, the model will always follow the exact schema defined in the `schema` field. Only a subset of JSON Schema is supported when `strict` is `true`. To learn more, read the [Structured Outputs guide](/docs/guides/structured-outputs).
    - ResponseFormatJsonObject
      - `type` 'json_object', required — The type of response format being defined. Always `json_object`.
  - `guided_json` object, nullable — JSON schema for guided decoding. Only supported in vllm.
  - `guided_regex` string, nullable — Regex for guided decoding. Only supported in vllm.
  - `guided_choice` string[], nullable — Choices for guided decoding. Only supported in vllm.
  - `guided_grammar` string, nullable — Context-free grammar for guided decoding. Only supported in vllm.
  - `guided_decoding_backend` string, nullable — If specified, will override the default guided decoding backend of the server for this specific request. If set, must be either 'outlines' / 'lm-format-enforcer'
  - `guided_whitespace_pattern` string, nullable — If specified, will override the default whitespace pattern for guided json decoding.
  - `model` string, required — ID of the model to use.
  - `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[], nullable — 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.
    - Prompt1Item[], nullable — 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.
      - integer[]
  - `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)
  - `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)
  - `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 — 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
    - string[], nullable — 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.
  - `stream` boolean, nullable — If set, partial message deltas will be sent. 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
    - `include_usage` boolean, nullable — 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. **NOTE:** If the stream is interrupted, you may not receive the final usage chunk which contains the total token usage for the request.
  - `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, nullable — 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`

Successful Response

- union
  - CreateCompletionResponse
    - `id` string, required — A unique identifier for the completion.
    - `choices` Choice2[], 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` Logprobs2, required
        - `text_offset` integer[], nullable
        - `token_logprobs` number[], nullable
        - `tokens` string[], nullable
        - `top_logprobs` object[], nullable
      - `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, nullable — 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
      - `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` CompletionTokensDetails
        - `accepted_prediction_tokens` integer — When using Predicted Outputs, the number of tokens in the prediction that appeared in the completion.
        - `audio_tokens` integer — Audio input tokens generated by the model.
        - `reasoning_tokens` integer — Tokens generated by the model for reasoning.
        - `rejected_prediction_tokens` integer — 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.
      - `prompt_tokens_details` PromptTokensDetails
        - `audio_tokens` integer — Audio input tokens present in the prompt.
        - `cached_tokens` integer — Cached tokens present in the prompt.
  - CompletionV2StreamErrorChunk
    - `error` StreamError, required — Error object for a stream prompt completion task.
      - `status_code` integer, required
      - `content` StreamErrorContent, required
        - `error` string, required
        - `timestamp` string, required

## Other responses

- `422` — Validation Error

---

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