How many lines do I need to change?

Two in the client setup, the base URL and the API key, plus the model name in your requests. Message format and response parsing stay the same.

Is it really ChatGPT without the filter?

No. It is a different open-weight model, run by an independent provider and not affiliated with OpenAI. It is tuned to answer without content refusals for lawful adult use.

Can I keep using LangChain or another framework?

Yes, as long as it lets you set a custom OpenAI-compatible base URL. Set the base URL, the key and the model ID uncensored.

What do I lose by switching?

Anything outside text: images, audio, embeddings and fine-tuning are not offered. You also get one model, not a menu.

ChatGPT Unfiltered Alternative: Switch in Two Lines

Before and after

Here is a typical OpenAI client in Python:

import os
from openai import OpenAI

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

r = client.chat.completions.create(
    model="your-current-model",
    messages=[{"role": "user", "content": "Draft a tense dialogue between two rivals."}],
)

And the same client pointed at the unfiltered alternative:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.chatgptunfiltered.com/v1",
    api_key=os.environ["UNFILTERED_API_KEY"],
)

r = client.chat.completions.create(
    model="uncensored",
    messages=[{"role": "user", "content": "Draft a tense dialogue between two rivals."}],
)

Two lines changed in the constructor, and the model string changed in the call. The message format, the response object and the way you read r.choices[0].message.content stay as they were. The alternative is a different model run by an independent provider. It is not ChatGPT and not an official API from OpenAI or anyone else.

Switching with environment variables only

Recent versions of the official Python and Node SDKs read OPENAI_BASE_URL and OPENAI_API_KEY when you create a client without arguments. That means the application code can stay untouched:

export OPENAI_BASE_URL="https://api.chatgptunfiltered.com/v1"
export OPENAI_API_KEY="YOUR_KEY"
python app.py

You still need to make sure the code passes model="uncensored". If the model name is hard-coded in many places, pull it into one config value first. That small refactor makes any future switch just as cheap, in either direction.

The same idea works in Node:

import OpenAI from "openai";

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

const r = await client.chat.completions.create({
  model: "uncensored",
  messages: [{ role: "user", content: "Give me five villain names." }],
});
console.log(r.choices[0].message.content);

Frameworks and the Responses format

Libraries that wrap the OpenAI client usually expose a base URL setting. In LangChain's OpenAI chat wrapper it looks like this:

from langchain_openai import ChatOpenAI

llm = ChatOpenAI(
    model="uncensored",
    base_url="https://api.chatgptunfiltered.com/v1",
    api_key="YOUR_KEY",
)
print(llm.invoke("Name three moods for a chase scene.").content)

If you use the newer Responses format, POST /v1/responses is supported as well as POST /v1/chat/completions. GET /v1/models lists the model, which is a quick check that your key and URL are right.

Differences to expect

Area	What you will notice
Willingness to answer	It answers without content refusals for lawful adult use. Prompts a filtered model declines will usually get an answer.
Wording and style	Different model, different voice. Tone, length and formatting habits will not match your previous model.
Model choice	One model, ID `uncensored`. No tiers to pick from.
Scope	Text only: no image, audio or video generation, no embeddings, no fine-tuning.
Context	100,000 tokens, prompt plus completion. `max_tokens` is optional.
Limits	Full key: 8 concurrent, 300 per minute. Trial key: 2 concurrent, 60 per minute. Body up to 8 MB.
Errors	HTTP 429 with code `rate_limited` or `concurrency`; temporary overload returns 429 with `Retry-After: 5`.

Streaming, tool calling and JSON mode are supported, as are temperature, top_p, stop, seed and the penalties. Response headers include X-Request-Id, X-Balance-USD, X-RateLimit-Limit-Requests and X-RateLimit-Limit-Concurrency. The documentation has the details.

Two further differences deserve a line. First, a different model can count tokens differently, so counts for the same text may differ from what your old provider reports; use the usage numbers in responses, not your own estimates. Second, features that depend on a vendor's hosted tools, such as built-in web search or file storage on the vendor side, are not part of this service. Tool calling is: you define functions, and your code runs them.

A parity script for your own prompts

Instead of eyeballing a handful of outputs, run your prompt set through the new endpoint in a loop and save everything you need to review.

import csv, json

with open("prompts.json", encoding="utf-8") as f:
    prompts = json.load(f)  # a list of strings

with open("results.csv", "w", newline="", encoding="utf-8") as out:
    w = csv.writer(out)
    w.writerow(["prompt", "reply", "in_tokens", "out_tokens"])
    for p in prompts:
        r = client.chat.completions.create(
            model="uncensored",
            temperature=0.7,
            messages=[{"role": "user", "content": p}],
        )
        w.writerow([p, r.choices[0].message.content, r.usage.prompt_tokens, r.usage.completion_tokens])

Open the CSV and review a column at a time. The token columns give you a cost forecast for the same traffic: multiply the sums by $0.15 and $0.50 per 1M. A trial key allows 60 requests per minute and 2 at once, so a set of a few hundred prompts finishes in minutes if you keep the loop sequential.

A short migration checklist

Get a key. On the API key page, sign up with Google or email and claim the $0.50 trial credit. It lasts 7 days and needs no card.
Move model name and URL into config. One place, read from the environment.
Replay your test prompts. Use 20 to 50 real examples, including the ones your previous model refused, and read the results.
Adjust the system prompt. Remove text that only existed to talk a filtered model into answering. Keep what describes role, audience and format.
Add a 429 handler. Wait 5 seconds and retry. Limit concurrency to match your key.
Check usage. Watch X-Balance-USD on responses while you run real traffic.

A retry helper that covers step 5:

import time
from openai import RateLimitError

def create_with_retry(**kwargs):
    for _ in range(4):
        try:
            return client.chat.completions.create(**kwargs)
        except RateLimitError:
            time.sleep(5)
    raise RuntimeError("rate limited after 4 tries")

Keep the old model's saved outputs for a few weeks. If a user complains that something feels off after the switch, you can compare against what the old model would have said and see whether the difference is the prompt, the model or the input.

Rolling out without risk

Do not flip everything at once. Put the choice of provider behind configuration so you can move a percentage of traffic and roll back in seconds.

import os, random
from openai import OpenAI

old = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
new = OpenAI(base_url="https://api.chatgptunfiltered.com/v1", api_key=os.environ["UNFILTERED_API_KEY"])

SHARE = float(os.environ.get("NEW_SHARE", "0.05"))

def pick():
    if random.random() < SHARE:
        return new, "uncensored"
    return old, os.environ["OLD_MODEL"]

Start at 5%, watch your error rate, 429 counts and user feedback for a few days, then raise it. Set NEW_SHARE to 0 to roll back. Choosing by user or by feature is better than choosing by request when conversations must stay on one model, since switching models mid-chat changes the voice.

What it costs, and when to stay put

Pricing is $0.15 per 1M input tokens and $0.50 per 1M output tokens, from prepaid credit. A request with 1,500 tokens in and 400 out costs 1,500 x $0.15 / 1M = $0.000225, plus 400 x $0.50 / 1M = $0.0002, so $0.000425, or $0.425 per 1,000 requests. For other vendors, check their current pricing pages; rates change and a comparison from memory goes stale quickly. Top-ups are whole amounts from $20 to $500, paid in crypto, with bonuses from $50. Details on pricing.

Staying with your current provider is the right call if you need images or embeddings, if your audience needs strict moderation, or if a vendor-specific model is a hard requirement. For background on what this class of model is, read understanding uncensored ChatGPT models. The hard limit never changes: no sexual content involving minors.

It is also fine to run both providers side by side. Many teams route ordinary requests to one model and the requests that keep hitting refusals to another. Because both speak the same format, that routing is a few lines of code and a rule for when to use which.