---
title: "Creates a completion for the provided prompt and parameters."
url: "https://kongair.terwilligar.com/apis/openai-api-2-3-0/versions/ea92d048-c746-4ead-a6a8-da28d93137d1/operations/createCompletion"
---

> Full API specification: https://kongair.terwilligar.com/apis/openai-api-2-3-0/versions/ea92d048-c746-4ead-a6a8-da28d93137d1.md

# Creates a completion for the provided prompt and parameters.

`POST` `/completions`

Operation ID: `createCompletion`

## Request body (required)

Content types: `application/json`

## Responses

- `200` - OK

## OpenAPI definition

```yaml
openapi: 3.0.0
info:
  title: OpenAI API
  version: 2.3.0
servers:
  - url: https://api.openai.com/v1
paths:
  /completions:
    post:
      operationId: createCompletion
      tags:
        - Completions
      summary: Creates a completion for the provided prompt and parameters.
      requestBody:
        required: true
        content:
          application/json:
            schema:
              $ref: "#/components/schemas/CreateCompletionRequest"
      responses:
        "200":
          description: OK
          content:
            application/json:
              schema:
                $ref: "#/components/schemas/CreateCompletionResponse"
      x-oaiMeta:
        name: Create completion
        group: completions
        returns: >
          Returns a [completion](/docs/api-reference/completions/object) object,
          or a sequence of completion objects if the request is streamed.
        legacy: true
        examples:
          - title: No streaming
            request:
              curl: |
                curl https://api.openai.com/v1/completions \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "model": "VAR_completion_model_id",
                    "prompt": "Say this is a test",
                    "max_tokens": 7,
                    "temperature": 0
                  }'
              python: |
                from openai import OpenAI
                client = OpenAI()

                client.completions.create(
                  model="VAR_completion_model_id",
                  prompt="Say this is a test",
                  max_tokens=7,
                  temperature=0
                )
              node.js: |-
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const completion = await openai.completions.create({
                    model: "VAR_completion_model_id",
                    prompt: "Say this is a test.",
                    max_tokens: 7,
                    temperature: 0,
                  });

                  console.log(completion);
                }
                main();
            response: |
              {
                "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
                "object": "text_completion",
                "created": 1589478378,
                "model": "VAR_completion_model_id",
                "system_fingerprint": "fp_44709d6fcb",
                "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
                }
              }
          - title: Streaming
            request:
              curl: |
                curl https://api.openai.com/v1/completions \
                  -H "Content-Type: application/json" \
                  -H "Authorization: Bearer $OPENAI_API_KEY" \
                  -d '{
                    "model": "VAR_completion_model_id",
                    "prompt": "Say this is a test",
                    "max_tokens": 7,
                    "temperature": 0,
                    "stream": true
                  }'
              python: |
                from openai import OpenAI
                client = OpenAI()

                for chunk in client.completions.create(
                  model="VAR_completion_model_id",
                  prompt="Say this is a test",
                  max_tokens=7,
                  temperature=0,
                  stream=True
                ):
                  print(chunk.choices[0].text)
              node.js: |-
                import OpenAI from "openai";

                const openai = new OpenAI();

                async function main() {
                  const stream = await openai.completions.create({
                    model: "VAR_completion_model_id",
                    prompt: "Say this is a test.",
                    stream: true,
                  });

                  for await (const chunk of stream) {
                    console.log(chunk.choices[0].text)
                  }
                }
                main();
            response: |
              {
                "id": "cmpl-7iA7iJjj8V2zOkCGvWF2hAkDWBQZe",
                "object": "text_completion",
                "created": 1690759702,
                "choices": [
                  {
                    "text": "This",
                    "index": 0,
                    "logprobs": null,
                    "finish_reason": null
                  }
                ],
                "model": "gpt-3.5-turbo-instruct"
                "system_fingerprint": "fp_44709d6fcb",
              }
security:
  - ApiKeyAuth: []
components:
  schemas:
    CreateCompletionRequest:
      type: object
      properties:
        model:
          description: >
            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) for
            descriptions of them.
          anyOf:
            - type: string
            - type: string
              enum:
                - gpt-3.5-turbo-instruct
                - davinci-002
                - babbage-002
          x-oaiTypeLabel: string
        prompt:
          description: >
            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.
          default: <|endoftext|>
          nullable: true
          oneOf:
            - type: string
              default: ""
              example: This is a test.
            - type: array
              items:
                type: string
                default: ""
                example: This is a test.
            - type: array
              minItems: 1
              items:
                type: integer
              example: "[1212, 318, 257, 1332, 13]"
            - type: array
              minItems: 1
              items:
                type: array
                minItems: 1
                items:
                  type: integer
              example: "[[1212, 318, 257, 1332, 13]]"
        best_of:
          type: integer
          default: 1
          minimum: 0
          maximum: 20
          nullable: true
          description: >
            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:
          type: boolean
          default: false
          nullable: true
          description: |
            Echo back the prompt in addition to the completion
        frequency_penalty:
          type: number
          default: 0
          minimum: -2
          maximum: 2
          nullable: true
          description: >
            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:
          type: object
          x-oaiTypeLabel: map
          default: null
          nullable: true
          additionalProperties:
            type: integer
          description: >
            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:
          type: integer
          minimum: 0
          maximum: 5
          default: null
          nullable: true
          description: >
            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:
          type: integer
          minimum: 0
          default: 16
          example: 16
          nullable: true
          description: |
            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:
          type: integer
          minimum: 1
          maximum: 128
          default: 1
          example: 1
          nullable: true
          description: >
            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:
          type: number
          default: 0
          minimum: -2
          maximum: 2
          nullable: true
          description: >
            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:
          type: integer
          format: int64
          nullable: true
          description: >
            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:
          description: >
            Up to 4 sequences where the API will stop generating further tokens.
            The returned text will not contain the stop sequence.
          default: null
          nullable: true
          oneOf:
            - type: string
              default: <|endoftext|>
              example: |+
                
              nullable: true
            - type: array
              minItems: 1
              maxItems: 4
              items:
                type: string
                example: '["\n"]'
        stream:
          description: |
            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).
          type: boolean
          nullable: true
          default: false
        stream_options:
          $ref: "#/components/schemas/ChatCompletionStreamOptions"
        suffix:
          description: |
            The suffix that comes after a completion of inserted text.

            This parameter is only supported for `gpt-3.5-turbo-instruct`.
          default: null
          nullable: true
          type: string
          example: test.
        temperature:
          type: number
          minimum: 0
          maximum: 2
          default: 1
          example: 1
          nullable: true
          description: >
            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:
          type: number
          minimum: 0
          maximum: 1
          default: 1
          example: 1
          nullable: true
          description: >
            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:
          type: string
          example: user-1234
          description: >
            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).
      required:
        - model
        - prompt
    CreateCompletionResponse:
      type: object
      description: >
        Represents a completion response from the API. Note: both the streamed
        and non-streamed response objects share the same shape (unlike the chat
        endpoint).
      properties:
        id:
          type: string
          description: A unique identifier for the completion.
        choices:
          type: array
          description: The list of completion choices the model generated for the input
            prompt.
          items:
            type: object
            required:
              - finish_reason
              - index
              - logprobs
              - text
            properties:
              finish_reason:
                type: string
                description: >
                  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.
                enum:
                  - stop
                  - length
                  - content_filter
              index:
                type: integer
              logprobs:
                type: object
                nullable: true
                properties:
                  text_offset:
                    type: array
                    items:
                      type: integer
                  token_logprobs:
                    type: array
                    items:
                      type: number
                  tokens:
                    type: array
                    items:
                      type: string
                  top_logprobs:
                    type: array
                    items:
                      type: object
                      additionalProperties:
                        type: number
              text:
                type: string
        created:
          type: integer
          description: The Unix timestamp (in seconds) of when the completion was created.
        model:
          type: string
          description: The model used for completion.
        system_fingerprint:
          type: string
          description: >
            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:
          type: string
          description: The object type, which is always "text_completion"
          enum:
            - text_completion
          x-stainless-const: true
        usage:
          $ref: "#/components/schemas/CompletionUsage"
      required:
        - id
        - object
        - created
        - model
        - choices
      x-oaiMeta:
        name: The completion object
        legacy: true
        example: |
          {
            "id": "cmpl-uqkvlQyYK7bGYrRHQ0eXlWi7",
            "object": "text_completion",
            "created": 1589478378,
            "model": "gpt-4-turbo",
            "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
            }
          }
    ChatCompletionStreamOptions:
      description: >
        Options for streaming response. Only set this when you set `stream:
        true`.
      type: object
      nullable: true
      default: null
      properties:
        include_usage:
          type: boolean
          description: >
            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.
    CompletionUsage:
      type: object
      description: Usage statistics for the completion request.
      properties:
        completion_tokens:
          type: integer
          default: 0
          description: Number of tokens in the generated completion.
        prompt_tokens:
          type: integer
          default: 0
          description: Number of tokens in the prompt.
        total_tokens:
          type: integer
          default: 0
          description: Total number of tokens used in the request (prompt + completion).
        completion_tokens_details:
          type: object
          description: Breakdown of tokens used in a completion.
          properties:
            accepted_prediction_tokens:
              type: integer
              default: 0
              description: |
                When using Predicted Outputs, the number of tokens in the
                prediction that appeared in the completion.
            audio_tokens:
              type: integer
              default: 0
              description: Audio input tokens generated by the model.
            reasoning_tokens:
              type: integer
              default: 0
              description: Tokens generated by the model for reasoning.
            rejected_prediction_tokens:
              type: integer
              default: 0
              description: >
                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:
          type: object
          description: Breakdown of tokens used in the prompt.
          properties:
            audio_tokens:
              type: integer
              default: 0
              description: Audio input tokens present in the prompt.
            cached_tokens:
              type: integer
              default: 0
              description: Cached tokens present in the prompt.
      required:
        - prompt_tokens
        - completion_tokens
        - total_tokens
  securitySchemes:
    ApiKeyAuth:
      type: http
      scheme: bearer
```