> ## Documentation Index
> Fetch the complete documentation index at: https://docs.myrouter.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Create Chat Completion

Generate a model response based on the specified chat conversation.

## Request Headers

<ParamField header="Content-Type" type="string" required={true}>
  Enum: `application/json`
</ParamField>

<ParamField header="Authorization" type="string" required={true}>
  Bearer authentication format: Bearer \{\{API Key}}.
</ParamField>

## Request Body

<ParamField body="model" type="string" required={true}>
  The name of the model to use.
</ParamField>

<ParamField body="messages" type="object[]" required={true}>
  A list of messages comprising the current conversation.

  <Expandable title="properties" defaultOpen={false}>
    <ParamField body="content" type="string | object[] | null" required={true}>
      The content of the message. All messages require content. For assistant messages containing function calls, content can be null.

      You can use the following parameters depending on the modality.

      <Frame>
        <div class="param_frame">
          <Tabs>
            <Tab title="Text Content">
              <p class="param_text">Option 1:</p>
              <p class="param_text">You can use a string type to represent the text content of the message.</p>

              <br />

              <p class="param_text">Option 2:</p>
              <p class="param_text">Use an array of content parts, object\[]. Detailed fields are as follows:</p>

              <ParamField body="type" type="string" required={true}>
                The type of the content part. In this case, `text`.
              </ParamField>

              <ParamField body="text" type="string" required={true}>
                The text content.
              </ParamField>
            </Tab>

            <Tab title="Image Content">
              <p class="param_text">Only available with vision-language models.</p>
              <p class="param_text">An array of content parts, object\[]. Detailed fields are as follows:</p>

              <ParamField body="type" type="string" required={true}>
                The type of the content part. In this case, `image_url`.
              </ParamField>

              <ParamField body="image_url" type="string" required={true}>
                <Expandable title="properties" defaultOpen={true}>
                  <ParamField body="url" type="string" required={true}>
                    The URL of the image or base64-encoded image data (Claude series models only support base64-encoded image data).
                  </ParamField>
                </Expandable>
              </ParamField>
            </Tab>

            <Tab title="Video Content">
              <p class="param_text">Only available with models that support video.</p>
              <p class="param_text">An array of content parts, object\[]. Detailed fields are as follows:</p>

              <ParamField body="type" type="string" required={true}>
                The type of the content part. In this case, `video_url`.
              </ParamField>

              <ParamField body="video_url" type="string" required={true}>
                <Expandable title="properties" defaultOpen={true}>
                  <ParamField body="url" type="string" required={true}>
                    The URL of the video.
                  </ParamField>
                </Expandable>
              </ParamField>
            </Tab>
          </Tabs>
        </div>
      </Frame>
    </ParamField>

    <ParamField body="role" type="string" required={true}>
      The role of the message author. Can be system, user, or assistant.

      Enum: `system`, `user`, `assistant`
    </ParamField>

    <ParamField body="name" type="string">
      The name of the author of this message. May contain a-z, A-Z, 0-9, and underscores, with a maximum length of 64 characters.
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="max_tokens" type="integer" required={true}>
  The maximum number of tokens to generate in the completion.

  If the number of tokens in your prompt (previous messages) plus max\_tokens exceeds the model's context length, the behavior depends on context\_length\_exceeded\_behavior. By default, max\_tokens will be reduced to fit the context window rather than returning an error.
</ParamField>

<ParamField body="stream" type="boolean | null" default={false}>
  Whether to stream partial progress. If set, tokens will be sent as data-only server-sent events (SSE) as they become available, and the stream will be terminated with a `data: [DONE]` message.
</ParamField>

<ParamField body="stream_options" type="object | null">
  Options for streaming responses. Only set this when stream is set to true.

  <Expandable title="properties" defaultOpen={false}>
    <ParamField body="include_usage" type="boolean">
      If set, an additional chunk will be streamed before the data: \[DONE] message. The usage field in this chunk shows the token usage statistics for the entire request, while the choices field is always an empty array. All other chunks will also include a usage field, but with a null value.
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="n" type="integer | null" default={1}>
  The number of completions to generate for each prompt.

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

  Required range: `1 < x < 128`
</ParamField>

<ParamField body="seed" type="integer | null">
  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.
</ParamField>

<ParamField body="frequency_penalty" type="number | null" default={0}>
  Positive values penalize new tokens based on their existing frequency in the text, decreasing the model's likelihood of repeating the same line verbatim.

  If the goal is only to slightly reduce repetitive samples, reasonable values are between 0.1 and 1. If the goal is to strongly suppress repetition, the coefficient can be increased to 2, but this may noticeably degrade sample quality. Negative values can be used to increase the likelihood of repetition.

  See also presence\_penalty, which penalizes tokens that have appeared at least once at a fixed rate.

  Required range: `-2 < x < 2`
</ParamField>

<ParamField body="presence_penalty" type="number | null" default={0}>
  Positive values penalize new tokens based on whether they appear in the text so far, increasing the model's likelihood of talking about new topics.

  If the goal is only to slightly reduce repetitive samples, reasonable values are between 0.1 and 1. If the goal is to strongly suppress repetition, the coefficient can be increased to 2, but this may noticeably degrade sample quality. Negative values can be used to increase the likelihood of repetition.

  See also `frequency_penalty`, which penalizes tokens at an increasing rate based on how often they appear.

  Required range: `-2 < x < 2`
</ParamField>

<ParamField body="repetition_penalty" type="number | null">
  Applies a penalty to repeated tokens to discourage or encourage repetition. A value of 1.0 means no penalty, allowing free repetition. Values above 1.0 penalize repetition, reducing the likelihood of repeated tokens. Values between 0.0 and 1.0 reward repetition, increasing the chance of repeated tokens. A value of 1.2 is generally recommended for a good balance. Note that the penalty applies to both the generated output and the prompt in decoder-only models.

  Required range: `0 < x < 2`
</ParamField>

<ParamField body="stop" type="string | null">
  Up to 4 sequences where the API will stop generating further tokens. The returned text will include the stop sequence.
</ParamField>

<ParamField body="temperature" type="number | null" default={1}>
  The 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.

  Required range: `0 < x < 2`
</ParamField>

<ParamField body="top_p" type="number | null">
  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.

  Required range: `0 < x <= 1`
</ParamField>

<ParamField body="top_k" type="integer | null">
  Top-k sampling is another sampling method where the k most likely next tokens are filtered and the probability mass is redistributed among only those k next tokens. The value of k controls the number of candidates for the next token at each step during text generation.

  Required range: `1 < x < 128`
</ParamField>

<ParamField body="min_p" type="number | null">
  Represents the minimum probability for a token to be considered, relative to the probability of the most likely token.

  Required range: `0 <= x <= 1`
</ParamField>

<ParamField body="logit_bias" type="map[string, integer] | null" required={false}>
  Modify the likelihood of specified tokens appearing in the completion.

  Accepts a JSON object that maps tokens to an associated bias value from -100 to 100.
  Mathematically, the bias is added to the logits generated by the model prior to sampling. The exact effect will vary per model.

  For example, setting `"logit_bias":{"1024": 6}` will increase the likelihood of the token with ID 1024.
</ParamField>

<ParamField body="logprobs" type="boolean | null" default={false}>
  Whether to return log probabilities of the output tokens. If true, returns the log probabilities of each output token in the message content.
</ParamField>

<ParamField body="top_logprobs" type="integer | null">
  An integer between 0 and 20 specifying the number of most likely tokens to return at each token position, each with an associated log probability. `logprobs` must be set to true if this parameter is used.

  Required range: `0 <= x <= 20`
</ParamField>

<ParamField body="tools" type="object[] | null">
  A list of tools the model may call. Currently, only functions are supported as tools. Use this to provide a list of functions the model may generate JSON inputs for.

  Learn more about function calling in the [Function Calling Guide](/docs/model/llm-function-calling).

  <Expandable title="properties" defaultOpen={false}>
    <ParamField body="type" type="string" required={true}>
      The type of the tool.

      Supported types: `function`
    </ParamField>

    <ParamField body="function" type="object" required={true}>
      <Expandable title="properties" defaultOpen={false}>
        <ParamField body="name" type="string" required={true}>
          The name of the function to be called. Must be a-z, A-Z, 0-9, or contain underscores and dashes, with a maximum length of 64.
        </ParamField>

        <ParamField body="description" type="string | null">
          A description of the function, used by the model to choose when and how to call the function.
        </ParamField>

        <ParamField body="parameters" type="object | null">
          The parameters the function accepts, described as a JSON Schema object. See the [JSON Schema reference](https://json-schema.org/understanding-json-schema/) for documentation about the format.
        </ParamField>

        <ParamField body="strict" type="boolean" default={false}>
          Whether to enable strict schema adherence when generating function calls. If set to true, the model will follow the exact schema defined in the parameters field.
        </ParamField>
      </Expandable>
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="response_format" type="object | null">
  Allows forcing the model to produce a specific output format.

  Set to `{ "type": "json_schema", "json_schema": {...} }` to enable Structured Outputs, which ensures the model will match your supplied JSON schema.

  Set to `{ "type": "json_object" }` to enable the legacy JSON mode, which ensures the model generates messages that are valid JSON. For models that support it, `json_schema` is recommended.

  <Expandable title="properties" defaultOpen={false}>
    <ParamField body="type" type="string" required={true} default="text">
      Enum: `text`, `json_object`, `json_schema`
    </ParamField>

    <ParamField body="json_schema" type="object | null">
      JSON Schema response format. Used to generate structured JSON responses.

      Only supported when `type` is set to `json_schema`, and also required when `type` is set to `json_schema`.

      Learn more in the [Structured Outputs Guide](/docs/model/llm-structured-outputs).

      <Expandable title="properties" defaultOpen={false}>
        <ParamField body="name" type="string" required={true}>
          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.
        </ParamField>

        <ParamField body="description" type="string | null">
          A description of the response format, used by the model to determine how to respond in that format.
        </ParamField>

        <ParamField body="schema" type="object | null">
          The schema of the response format, described as a JSON Schema object. Learn how to build JSON schemas [here](https://json-schema.org/specification).

          Supported types: `string`, `number`, `integer`, `boolean`, `array`, `object`, `enum`, `anyOf`.
        </ParamField>

        <ParamField body="strict" type="boolean" default={false}>
          Whether to enable strict schema adherence when generating 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.

          If you enable Structured Outputs by providing `strict: true` and call the API with an unsupported JSON Schema, you will receive an error.
        </ParamField>
      </Expandable>
    </ParamField>
  </Expandable>
</ParamField>

<ParamField body="separate_reasoning" type="boolean | null" default={false}>
  Whether to separate reasoning from "content" into the "reasoning\_content" field.

  Supported models:

  * `deepseek/deepseek-r1-turbo`
</ParamField>

<ParamField body="enable_thinking" type="boolean | null" default={true}>
  Controls switching between thinking and non-thinking modes.

  Supported models:

  * `zai-org/glm-4.5`
</ParamField>

## Response

<ResponseField name="choices" type="object[]" required={true}>
  A list of chat completion choices.

  <Expandable title="properties" defaultOpen={false}>
    <ResponseField name="finish_reason" type="string" required={true}>
      The reason the model stopped generating tokens. This will be "stop" if the model hit a natural stop point or a provided stop sequence, or "length" if the maximum number of tokens specified in the request was reached.

      Options: `stop`, `length`
    </ResponseField>

    <ResponseField name="index" type="integer" required={true}>
      The index of the chat completion choice.
    </ResponseField>

    <ResponseField name="message" type="object" required={true}>
      <Expandable title="properties" defaultOpen={false}>
        <ResponseField name="role" type="string" required={true}>
          The role of the author of this message.

          Options: `system`, `user`, `assistant`
        </ResponseField>

        <ResponseField name="content" type="string | null">
          The content of the message.
        </ResponseField>

        <ResponseField name="reasoning_content" type="string | null">
          The content of the reasoning steps.

          <Warning>
            This field is only available when `separate_reasoning` is set to true.
          </Warning>
        </ResponseField>
      </Expandable>
    </ResponseField>
  </Expandable>
</ResponseField>

<ResponseField name="created" type="integer" required={true}>
  The Unix timestamp (in seconds) of when the response was generated.
</ResponseField>

<ResponseField name="id" type="string" required={true}>
  A unique identifier for the response.
</ResponseField>

<ResponseField name="model" type="string" required={true}>
  The model used for the chat completion.
</ResponseField>

<ResponseField name="object" type="string" required={true}>
  The object type, always `chat.completion`.
</ResponseField>

<ResponseField name="usage" type="object">
  Usage statistics.

  For streaming responses, the usage field is included in the last response chunk returned.

  <Expandable title="properties" defaultOpen={false}>
    <ResponseField name="completion_tokens" type="integer" required={true}>
      Number of tokens in the generated completion.
    </ResponseField>

    <ResponseField name="prompt_tokens" type="integer" required={true}>
      Number of tokens in the prompt.
    </ResponseField>

    <ResponseField name="total_tokens" type="integer" required={true}>
      Total number of tokens used in the request (prompt + completion).
    </ResponseField>
  </Expandable>
</ResponseField>