Last Update: 2025/3/26

Anthropic Completions API

The Anthropic Completions API allows you to generate text completions using Anthropic's language models. This document provides an overview of the API endpoints, request parameters, and response structure.

Endpoint

POST https://platform.llmprovider.ai/v1/complete

Request Headers

Header	Value
x-api-key	YOUR_API_KEY
anthropic-version	2023-06-01
Content-Type	application/json

Request Body

The request body should be a JSON object with the following parameters:

Parameter	Type	Description
max_tokens_to_sample	integer	The maximum number of tokens to generate before stopping. Required range: x > 1
model	string	The model that will complete your prompt. (e.g., claude-2.1).
prompt	string	The prompt to complete.
metadata	object	(Optional) An object describing metadata about the request.
stop_sequences	string[]	(Optional) Sequences that will cause the model to stop generating.
stream	boolean	(Optional) Whether to stream the response using server-sent events.
temperature	number	(Optional) Amount of randomness injected into the response. Required range: 0 < x < 1
top_k	number	(Optional) Only sample from top K options. Required range: x > 0
top_p	number	(Optional) Use nucleus sampling. Required range: 0 < x < 1
max_tokens	integer	(Optional) The maximum number of tokens to generate.

Example Request

{
  "model": "claude-2.1",
  "max_tokens_to_sample": 1024,
  "prompt": "\n\nHuman: Hello, Claude\n\nAssistant:"
}

Response Body

The response body will be a JSON object containing the completion and metadata.

Field	Type	Description
id	string	Unique identifier for the completion.
model	string	The model that handled the request.
completion	string	The generated completion text.
stop_reason	string	The reason why the completion stopped.
type	string	The type of completion.

Example Response

{
  "completion": " Hello! My name is Claude.",
  "id": "compl_018CKm6gsux7P8yMcwZbeCPw",
  "model": "claude-2.1",
  "stop_reason": "stop_sequence",
  "type": "completion"
}

Example Request

Shell

curl -X POST https://platform.llmprovider.ai/v1/complete \
-H "x-api-key: $YOUR_API_KEY" \
-H "anthropic-version: 2023-06-01" \
-H "Content-Type: application/json" \
-d '{
    "model": "claude-2.1",
    "max_tokens_to_sample": 1024,
    "prompt": "\n\nHuman: Hello, Claude\n\nAssistant:"
}'

nodejs

const axios = require('axios');
const apiKey = 'YOUR_API_KEY';
const url = 'https://platform.llmprovider.ai/v1/complete';

const data = {
    model: 'claude-3-5-sonnet-20241022',
    max_tokens_to_sample: 1024,
    prompt: '\n\nHuman: Hello, Claude\n\nAssistant:'
};

const headers = {
    'x-api-key': apiKey,
    'anthropic-version': '2023-06-01',
    'Content-Type': 'application/json'
};

axios.post(url, data, { headers })
.then(response => {
    console.log('Response:', response.data);
})
.catch(error => {
    console.error('Error:', error);
});

python

import requests
import json

api_key = 'YOUR_API_KEY'
url = 'https://platform.llmprovider.ai/v1/complete'

headers = {
    'x-api-key': api_key,
    'anthropic-version': '2023-06-01',
    'Content-Type': 'application/json'
}

data = {
    'model': 'claude-3-5-sonnet-20241022',
    'max_tokens_to_sample': 1024,
    'prompt': '\n\nHuman: Hello, Claude\n\nAssistant:'
}

response = requests.post(url, headers=headers, data=json.dumps(data))

if response.status_code == 200:
    print('Response:', response.json())
else:
    print('Error:', response.status_code, response.text)

For more details, refer to the Anthropic API documentation.