DeepSeek R1
Both IDs listed above refer to the same model; we support them for backward compatibility.
Model Overview
DeepSeek R1 is a cutting-edge reasoning model developed by DeepSeek AI, designed to excel in complex problem-solving, mathematical reasoning, and programming assistance. Leveraging a Mixture-of-Experts (MoE) architecture, the model activates only a subset of its parameters for each token processed, allowing for efficient computation while maintaining high performance across various tasks.
How to Make a Call
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.
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.
(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.
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
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.
512falsePOST /v1/chat/completions HTTP/1.1
Host: api.apilaplas.com
Authorization: Bearer <YOUR_LAPLASAPI_KEY>
Content-Type: application/json
Accept: */*
Content-Length: 886
{
"model": "deepseek/deepseek-r1",
"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
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":"deepseek/deepseek-r1",
"messages":[
{
"role":"user",
# Insert your question for the model here, instead of Hello:
"content":"Hello"
}
]
}
)
data = response.json()
print(data)Last updated