o1

This documentation is valid for the following list of our models:

• o1

Model Overview

A state-of-the-art language model designed to excel in complex reasoning tasks, including mathematical problem-solving, programming challenges, and scientific inquiries. The model integrates advanced reasoning capabilities through its innovative architecture, making it suitable for a wide range of applications that require deep understanding and logical deduction.

How to Make a Call

1

Setup You Can’t Skip

▪️ 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.

2

Copy the code example

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.

3

Modify the code example

▪️ Replace <YOUR_LAPLASAPI_KEY> with your actual Apilaplas API key from your account. ▪️ Insert your question or request into the content field—this is what the model will respond to.

4

^_(Optional) Adjust other optional parameters if needed

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.

5

Run your modified code

Run your modified code in your development environment. Response time depends on various factors, but for simple prompts it rarely exceeds a few seconds.

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.

API Schema

Generate a conversational response using a language model.

post

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.

Authorizations

Body

modelundefined · enumRequiredPossible values:

frequency_penaltynumber | nullableOptional

logprobsboolean | nullableOptional

top_logprobsnumber | nullableOptional

max_tokensnumber · min: 1OptionalDefault: 512

max_completion_tokensinteger · min: 1Optional

ninteger | nullableOptional

presence_penaltynumber | nullableOptional

seedinteger · min: 1Optional

streamboolean · enumOptionalDeprecatedDefault: falsePossible values:

top_pnumber · min: 0.1 · max: 1Optional

temperaturenumber · max: 2Optional

stopany ofOptional

stringOptional

or

string[]Optional

or

any | nullableOptional

tool_choiceany ofOptional

string · enumOptionalPossible values:

or

parallel_tool_callsbooleanOptional

reasoning_effortstring · enumOptionalPossible values:

response_formatone ofOptional

or

or

Responses

201Success

post

HTTPcURLJavaScriptPython

POST /v1/chat/completions HTTP/1.1
Host: api.apilaplas.com
Authorization: Bearer <YOUR_LAPLASAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 851

{
  "model": "o1",
  "frequency_penalty": 1,
  "logit_bias": {
    "ANY_ADDITIONAL_PROPERTY": 1
  },
  "logprobs": true,
  "top_logprobs": 1,
  "max_tokens": 1,
  "max_completion_tokens": 1,
  "n": 1,
  "prediction": {
    "type": "content",
    "content": "text"
  },
  "presence_penalty": 1,
  "seed": 1,
  "messages": [
    {
      "role": "system",
      "content": "text",
      "name": "text"
    }
  ],
  "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"
    }
  }
}

201Success

No content

Code Example (Python)

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":"o1",
        "messages":[
            {
                "role":"user",

                # Insert your question for the model here, instead of Hello:
                "content":"Hello"
            }
        ]
    }
)

data = response.json()
print(data)

Response

{'id': 'chatcmpl-BKKmwhCaUyWjRrBdZLw0CjOwJq9wo', 'object': 'chat.completion', 'choices': [{'index': 0, 'finish_reason': 'stop', 'logprobs': None, 'message': {'role': 'assistant', 'content': 'Hello there! How can I help you today?', 'refusal': None, 'annotations': []}}], 'created': 1744186170, 'model': 'o1-2024-12-17', 'usage': {'prompt_tokens': 221, 'completion_tokens': 2646, 'total_tokens': 2867, 'prompt_tokens_details': {'cached_tokens': 0, 'audio_tokens': 0}, 'completion_tokens_details': {'reasoning_tokens': 0, 'audio_tokens': 0, 'accepted_prediction_tokens': 0, 'rejected_prediction_tokens': 0}}, 'system_fingerprint': 'fp_688960522e'}