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POST
/
openai
/
v1
/
chat
/
completions
Create Chat Conversation Request
curl --request POST \
  --url https://api.highwayapi.ai/openai/v1/chat/completions \
  --header 'Authorization: <authorization>' \
  --header 'Content-Type: <content-type>' \
  --data '
{
  "model": "<string>",
  "messages": [
    {
      "content": {
        "type": "<string>",
        "text": "<string>",
        "image_url": "<string>",
        "video_url": "<string>"
      },
      "role": "<string>",
      "name": "<string>"
    }
  ],
  "max_tokens": 123,
  "stream": {},
  "stream_options": {
    "include_usage": true
  },
  "n": {},
  "seed": {},
  "frequency_penalty": {},
  "presence_penalty": {},
  "repetition_penalty": {},
  "stop": {},
  "temperature": {},
  "top_p": {},
  "top_k": {},
  "min_p": {},
  "logit_bias": {},
  "logprobs": {},
  "top_logprobs": {},
  "tools": {
    "type": "<string>",
    "function": {
      "name": "<string>",
      "description": {},
      "parameters": {},
      "strict": true
    }
  },
  "response_format": {
    "type": "<string>",
    "json_schema": {
      "name": "<string>",
      "description": {},
      "schema": {},
      "strict": true
    }
  },
  "separate_reasoning": {},
  "enable_thinking": {}
}
'
{
  "choices": [
    {
      "finish_reason": "<string>",
      "index": 123,
      "message": {
        "role": "<string>",
        "content": {},
        "reasoning_content": {}
      }
    }
  ],
  "created": 123,
  "id": "<string>",
  "model": "<string>",
  "object": "<string>",
  "usage": {
    "completion_tokens": 123,
    "prompt_tokens": 123,
    "total_tokens": 123
  }
}
Generate a model response based on the specified chat conversation

​
Request Headers

​
Content-Type
string
required
Enum value: application/json
​
Authorization
string
required
Bearer authentication format: Bearer {{API Key}}.

​
Request Body

​
model
string
required
The name of the model to use.
​
messages
object[]
required
A list of messages that make up the current conversation.
​
max_tokens
integer
required
The maximum number of tokens to generate in the completion.If 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 within the context window instead of returning an error.
​
stream
boolean | null
default:false
Whether to return partial progress as a stream. If set, tokens will be sent as data-only server-sent events (SSE) as they become available, and the stream will terminate with a data: [DONE] message.
​
stream_options
object | null
Options for the streaming response. Set this only when stream is set to true.
​
n
integer | null
default:1
The number of completions to generate for each prompt.Note: Because this parameter generates many completions, it may quickly consume your token quota. Use it with caution, and make sure you have reasonable settings for max_tokens and stop.Required range: 1 < x < 128
​
seed
integer | null
If specified, our system will make a best effort to sample deterministically, so repeated requests with the same seed and parameters should return the same result.
​
frequency_penalty
number | null
default:0
Positive values penalize new tokens based on their existing frequency in the text, reducing the likelihood that the model will repeat 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 significantly 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
​
presence_penalty
number | null
default:0
Positive values penalize new tokens based on whether they appear in the text, increasing the likelihood that the model will talk 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 significantly 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 frequently they appear.Required range: -2 < x < 2
​
repetition_penalty
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. For a good balance, a value of 1.2 is usually recommended. Note that the penalty applies to both the generated output and the prompt in decoder-only models.Required range: 0 < x < 2
​
stop
string | null
Up to 4 sequences where the API will stop generating further tokens. The returned text will contain the stop sequence.
​
temperature
number | null
default:1
The sampling temperature to use, between 0 and 2. Higher values such as 0.8 make the output more random, while lower values such as 0.2 make it more focused and deterministic.We generally recommend changing this or top_p, but not both.Required range: 0 < x < 2
​
top_p
number | null
An alternative to sampling with temperature, called nucleus sampling, where the model considers token results with top_p probability mass. Thus, 0.1 means only the tokens comprising the top 10% probability mass are considered. We generally recommend changing this or temperature, but not both.Required range: 0 < x <= 1
​
top_k
integer | null
Top-k sampling is another sampling method where the k most likely next tokens are filtered, and the probability mass is redistributed only among these k next tokens. The value of k controls the number of candidate next tokens at each step during text generation.Required range: 1 < x < 128
​
min_p
number | null
Represents the minimum probability for tokens to be considered, relative to the probability of the most likely token.Required range: 0 <= x <= 1
​
logit_bias
map[string, integer] | null
Modifies the likelihood of specified tokens appearing in the completion.Accepts a JSON object that maps tokens to associated bias values between -100 and 100. Mathematically, the bias is added to the logits generated by the model before sampling. The exact effect will vary by model.For example, setting "logit_bias":{"1024": 6} will increase the likelihood of tokens with token ID 1024.
​
logprobs
boolean | null
default:false
Whether to return the log probabilities of output tokens. If true, the log probability of each output token in the message content is returned.
​
top_logprobs
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. If this parameter is used, logprobs must be set to true.Required range: 0 <= x <= 20
​
tools
object[] | null
A list of tools the model can call. Currently, only functions are supported as tools. Use this to provide a list of functions for which the model can generate JSON inputs.Learn more about function calling in the Function Calling Guide.
​
response_format
object | null
Allows forcing the model to generate a specific output format.Set to { "type": "json_schema", "json_schema": {...} } to enable structured outputs and ensure the model will match the JSON schema you provide.Set to { "type": "json_object" } to enable the legacy JSON mode, ensuring that messages generated by the model are valid JSON. For models that support it, json_schema is recommended.
​
separate_reasoning
boolean | null
default:false
Whether to separate reasoning from “content” into the “reasoning_content” field.Supported models:
  • deepseek/deepseek-r1-turbo
​
enable_thinking
boolean | null
default:true
Controls switching between thinking and non-thinking modes.Supported models:
  • zai-org/glm-4.5

​
Response Information

​
choices
object[]
required
The list of chat completion choices.
​
created
integer
required
The Unix time when the response was generated, in seconds.
​
id
string
required
The unique identifier of the response.
​
model
string
required
The model used for the chat completion.
​
object
string
required
The object type, always chat.completion.
​
usage
object
Usage statistics.For streaming responses, the usage field is included in the last response chunk returned.