gpt-4-preview
Last updated
Last updated
Before the release of GPT-4 Turbo, OpenAI introduced two preview models that allowed users to test advanced features ahead of a full rollout. These models supported JSON mode for structured responses, parallel function calling to handle multiple API functions in a single request, and reproducible output, ensuring more consistent results across runs. They provided a glimpse into upcoming improvements in efficiency and functionality, helping developers and businesses adapt to the evolving capabilities of OpenAI's language models. gpt-4-1106-preview
has better code generation performance, reduces cases where the model doesn't complete a task.
Create an Account: Visit the Apilaplas API website and create an account (if you don’t have one yet). Generate an API Key: After logging in, navigate to your account dashboard and generate your API key. Ensure that key is enabled on UI.
At the bottom of this page, you'll find a code example that shows how to structure the request. Choose the code snippet in your preferred programming language and copy it into your development environment.
Only model
and messages
are required parameters for this model (and we’ve already filled them in for you in the example), but you can include optional parameters if needed to adjust the model’s behavior. Below, you can find the corresponding API schema, which lists all available parameters along with notes on how to use them.
If you need a more detailed walkthrough for setting up your development environment and making a request step by step — feel free to use our Quickstart guide.
import requests
response = requests.post(
"https://api.apilaplas.com/v1/chat/completions",
headers={
"Content-Type":"application/json",
# Insert your LAPLAS API Key instead of <YOUR_LAPLASAPI_KEY>:
"Authorization":"Bearer <YOUR_LAPLASAPI_KEY>",
"Content-Type":"application/json"
},
json={
"model":"gpt-4-1106-preview",
"messages":[
{
"role":"user",
# Insert your question for the model here, instead of Hello:
"content":"Hello"
}
]
}
)
data = response.json()
print(data)
Creates a chat completion using a language model, allowing interactive conversation by predicting the next response based on the given chat history. This is useful for AI-driven dialogue systems and virtual assistants.
512
false
POST /v1/chat/completions HTTP/1.1
Host: api.apilaplas.com
Authorization: Bearer <YOUR_LAPLASAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 884
{
"model": "gpt-4-0125-preview",
"frequency_penalty": 1,
"logit_bias": {
"ANY_ADDITIONAL_PROPERTY": 1
},
"logprobs": true,
"top_logprobs": 1,
"max_tokens": 512,
"max_completion_tokens": 1,
"n": 1,
"prediction": {
"type": "content",
"content": "text"
},
"presence_penalty": 1,
"seed": 1,
"messages": [
{
"role": "system",
"content": "text",
"name": "text"
}
],
"stream": false,
"stream_options": {
"include_usage": true
},
"top_p": 1,
"temperature": 1,
"stop": "text",
"tools": [
{
"type": "function",
"function": {
"description": "text",
"name": "text",
"parameters": null,
"strict": true,
"required": [
"text"
]
}
}
],
"tool_choice": "none",
"parallel_tool_calls": true,
"reasoning_effort": "low",
"response_format": {
"type": "text"
},
"audio": {
"format": "wav",
"voice": "alloy"
},
"modalities": [
"text"
],
"web_search_options": {
"search_context_size": "low",
"user_location": {
"approximate": {
"city": "text",
"country": "text",
"region": "text",
"timezone": "text"
},
"type": "approximate"
}
}
}
No content