Claude ai prezzo: API Quickstart

Base URL and Authentication

Send requests to our proxy endpoint using standard HTTP headers. The base URL is https://api.claudeaiprezzo.com/v1Authorization header. This key is generated instantly when you sign up. It works with any library that supports OpenAI-compatible clients. You do not need to install custom drivers. Just point your SDK to our domain.

Sending Your First Request

Make a simple text completion to verify connectivity. Use the uncensored model ID for unrestricted outputs. You can send prompts in any language. The API returns a direct text response. There are no hidden fees for failed attempts. Errors do not consume your credit. You can test this with a simple cURL command.

Copycurl https://api.claudeaiprezzo.com/v1/chat/completions \
  -H "Authorization: Bearer $API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "List the standard pricing tiers for usage in euros."}]
  }'

The response includes the generated text and token counts. You can see exactly what you are paying for. Input and output tokens are billed separately. The rate is transparent and fixed. No subscription locks you in.

Python SDK Integration

Use the official OpenAI Python package for easy integration. Set the base URL to our API endpoint. Pass your API key as the client value. This method works for most standard use cases. You can stream results or get full responses. The code is minimal and readable. It fits into existing projects quickly.

Copyfrom openai import OpenAI

client = OpenAI(base_url="https://api.claudeaiprezzo.com/v1", api_key="YOUR_KEY")

resp = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Summarise this thread without softening it."}],
)
print(resp.choices[0].message.content)

Import the library and initialize the client. Then call the chat completions function. Pass your system prompt and user message. The model returns the completion. You can adjust parameters like temperature. This gives you control over creativity. The response structure matches OpenAI standards.

Node SDK Setup

Install the OpenAI Node.js package via npm. Configure the base URL to point to our server. Authenticate with your unique API key. This approach is ideal for web servers. You can handle concurrent requests easily. The SDK manages retries and errors. It fits well with Express or Fastify. You can integrate it into your backend.

Copyimport OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.claudeaiprezzo.com/v1", apiKey: process.env.API_KEY });

const resp = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Draft a villain monologue for my game." }],
});
console.log(resp.choices[0].message.content);

Create an instance of the OpenAI client. Set the environment variables for security. Call the chat endpoint with your message. The response contains the generated text. You can parse the JSON directly. This works for both Node and Deno. It is a standard, reliable pattern.

Streaming Responses

Enable streaming by setting the stream parameter to true. The API sends data in chunks via SSE. Each chunk contains a fragment of the text. This reduces perceived latency for users. You can display tokens as they arrive. The final chunk includes token usage data. This helps you track costs accurately. Streaming is essential for chat interfaces.

Copystream = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Tell the story in second person."}],
    stream=True,
)
for chunk in stream:
    if chunk.choices and chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Handle each chunk in your client code. Append the text to your display buffer. Wait for the final chunk to get totals. This method works with any SSE client. It provides a smooth user experience. The model does not wait for full generation. You see progress in real time.

Rate Limits and Context Windows

You get 300 requests per minute per key. This limit is generous for most developers. You can run 8 concurrent requests at once. The request body must stay under 8 MB. If you exceed the limit, you get a 429 error. This does not charge your credit. You just need to wait briefly. The context window is 100,000 tokens. This includes both input and output. You can send long documents. The model retains context deeply. Max output is 16,000 tokens. If you do not set max_tokens, it defaults to 2,048. Errors like invalid keys return 401. Low credit returns 402. These are free to check. Your credit never expires. You pay only for what you use. This model is uncensored and direct.