abab6.5s-chat

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

• abab6.5s-chat

Model Overview

A powerful language model developed by MiniMax AI, designed to excel in tasks requiring extensive context processing and reasoning capabilities. With a total of 456 billion parameters, of which 45.9 billion are activated per token, this model utilizes a hybrid architecture that combines various attention mechanisms to optimize performance across a wide array of applications. Achieves competitive scores on academic benchmarks, including MMLU and various reasoning tests.

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:

max_tokensnumber · min: 1OptionalDefault: 256

streambooleanOptionalDefault: false

temperaturenumber · max: 1OptionalDefault: 0.1

top_pnumber · min: 0.01 · max: 1OptionalDefault: 0.95

mask_sensitive_infobooleanOptionalDefault: false

tool_choiceany ofOptional

string · enumOptionalPossible values:

or

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: 329

{
  "model": "abab6.5s-chat",
  "messages": [
    {
      "role": "system",
      "content": "text",
      "name": "text"
    }
  ],
  "max_tokens": 256,
  "stream": false,
  "temperature": 0.1,
  "top_p": 0.95,
  "mask_sensitive_info": false,
  "tools": [
    {
      "type": "function",
      "function": {
        "description": "text",
        "name": "text",
        "parameters": null
      }
    }
  ],
  "tool_choice": "none",
  "response_format": {
    "type": "text"
  }
}

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":"abab6.5s-chat",
        "messages":[
            {
                "role":"user",

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

data = response.json()
print(data)

Response

{'id': '04457c4318600c7c5d535374211265b5', 'object': 'chat.completion', 'choices': [{'index': 0, 'finish_reason': 'stop', 'message': {'role': 'assistant', 'content': 'Hello! How can I assist you today?'}}], 'created': 1744193859, 'model': 'abab6.5s-chat', 'usage': {'prompt_tokens': 22, 'completion_tokens': 4, 'total_tokens': 26}}