Summary
- Free access comes as provider tiers, trial credit, community endpoints or self-hosting.
- The $0.50 trial covers about 1,250 short chat turns or 62 long-document requests.
- The trial lasts 7 days, needs no payment details and is limited to 2 concurrent requests.
- After the trial, the smallest top-up is $20 and the credit never expires.
Four kinds of free
Running a large language model takes GPUs, and someone pays for them. That is why "free" nearly always has a shape. The shapes you will meet:
| Kind | What is free | What to check |
|---|---|---|
| Provider free tier | A monthly or daily allowance | Rate limits, model choice, whether prompts may be used for training, content rules |
| Trial credit | A small prepaid balance | Expiry date, one per person or per payment method, request limits |
| Community or shared endpoint | Access on someone else's hardware | Queues, uptime, who can read your prompts |
| Self-hosted open weights | The software | The GPU, power, setup time and keeping it running |
None of these is wrong. They answer different needs, and the right one depends on how long you plan to use it and what you send through it. Check each provider's current terms, because free tiers change.
For a developer, the question that cuts through most of this is whether the free option speaks the OpenAI chat format. If it does, your test code transfers to any other provider with a changed base URL, and the time you spend on a free tier is never wasted.
What the $0.50 trial is
New users can claim $0.50 of trial credit with one click on the API key page, after signing up with Google or with an email and password. It is one per person, valid for 7 days, and it needs no payment details. The credit is real balance on the same endpoint and the same model (uncensored) that paying users get, so what you test is what you will run later.
The trial key has its own limits: 2 concurrent requests and 60 requests per minute, against 8 and 300 for a full key. Failed or refused requests are not charged, which matters when you are poking at edge cases.
What $0.50 buys, worked out
Pricing is $0.15 per 1M input tokens and $0.50 per 1M output tokens. Cost per request is input tokens x 0.15 / 1,000,000 plus output tokens x 0.50 / 1,000,000. Here is how $0.50 stretches across different workloads:
| Workload | Tokens in / out | Cost per request | Requests per $0.50 |
|---|---|---|---|
| Short chat turn | 1,000 / 500 | $0.0004 | 1,250 |
| Chat with history | 2,000 / 800 | $0.0007 | 714 |
| Document Q&A | 6,000 / 600 | $0.0012 | 416 |
| Long document | 50,000 / 1,000 | $0.008 | 62 |
The long document line is 50,000 x $0.15 / 1M = $0.0075, plus 1,000 x $0.50 / 1M = $0.0005, total $0.008. The context window is 100,000 tokens for prompt plus completion, so even a request that fills it costs only a few cents.
The numbers assume the prompt grows with the conversation, as it does in a real chat. A reply of 500 tokens is roughly 375 English words, so the first row is a fairly long answer. If your replies are shorter, the same credit lasts longer.
A seven-day test plan
Seven days is short, so spend them on questions that decide whether to continue. A sensible order:
- Day 1. Send your real prompts, not toy ones. Check that the answers fit your use case and that nothing is refused that should not be.
- Day 2. Turn on streaming and time the first visible token in your own interface.
- Day 3. Test tool calling or JSON mode if your app depends on structured output.
- Days 4 to 5. Replay a day of typical traffic at a low rate and read the
X-Balance-USDheader to see the real cost per user action. - Day 6. Provoke a 429 on purpose by opening three streams at once, and confirm your code backs off.
The docs on streaming, tools and limits cover each of these. A minimal streaming test:
import os
from openai import OpenAI
client = OpenAI(base_url="https://api.llmwithoutlogin.com/v1", api_key=os.environ["API_KEY"])
stream = client.chat.completions.create(
model="uncensored",
messages=[{"role": "user", "content": "Write a four-line poem about rain."}],
stream=True,
)
for chunk in stream:
if chunk.choices and chunk.choices[0].delta.content:
print(chunk.choices[0].delta.content, end="", flush=True)Measuring the real cost of one request
Guessing token counts is the main reason cost estimates go wrong. Every non-streamed reply includes a usage block, and a streamed one reports usage at the end. Turn it into dollars with the two prices:
import os
from openai import OpenAI
client = OpenAI(base_url="https://api.llmwithoutlogin.com/v1", api_key=os.environ["API_KEY"])
r = client.chat.completions.create(
model="uncensored",
messages=[{"role": "user", "content": "Summarize the plot of Hamlet in 100 words."}],
)
u = r.usage
cost = u.prompt_tokens * 0.15 / 1_000_000 + u.completion_tokens * 0.50 / 1_000_000
print(u.prompt_tokens, u.completion_tokens, "tokens, about $%.6f" % cost)Run this on twenty of your real prompts, take the average, and multiply by the number of requests you expect per user per day. That one figure tells you more than any price table, and you will have it before the trial credit is half gone.
What a trial cannot tell you
Be realistic about what 7 days and 2 concurrent requests can show. You will not learn how the service behaves under a launch-day spike, and a trial key will hit its 60 requests per minute ceiling long before a full key does. Treat the trial as a check of fit and cost, not a load test.
It also does not cover anything outside text. This API does text generation only: no images, audio, video, embeddings or fine-tuning. If your project needs those, a free option for them has to come from elsewhere.
Before you rely on any free API, find written answers to four questions: what is the rate limit, is there a daily or monthly cap, which model sits behind the endpoint, and are prompts kept or used for training? An offer that answers all four on one page is easy to compare. One that hides them usually has a catch, and discovering it after you built a product around the offer is the expensive way to learn.
Keep a plain file with the prompts you test and the replies you get, plus the X-Balance-USD value before and after each batch. Later you can run the same prompts against another provider and compare quality and cost side by side.
When free runs out
Free access is a way to decide, not a plan. When the trial ends, you pay only for what you use, from prepaid credit. Top-ups are whole amounts from $20 to $500, with +5% from $50, +10% from $100 and +15% from $200. Payment is crypto only, USDT on TRC20 or USDC on Base. Credit never expires and there is no subscription.
At $20 and the 1,000 in, 500 out turn from above, that is $20 / $0.0004 = 50,000 turns. The pricing page has the exact bonus table if you want to plan a larger top-up. If you want an overview of what the model will and will not do, our article on AI without restrictions covers it.
More guides
Common Questions About Uncensored AI
Common questions about uncensored AI focus on reliability, content limits, and pricing. Users often ask if the model will refuse their requests. The answer is no, unless the content involves sexual material with minors. This is the only hard limit. All other topics, including mature themes, are fair game.
Another question is about data privacy. Your prompts are not used for training. This is crucial for developers who need to keep their data secure. You can use the API for proprietary applications without worrying about your data being leaked. The model runs on our servers, but your data is not stored long-term for training purposes.
Users also ask about the signup process. You can sign up with Google or an email address. No phone number is required. This makes it easy to get started quickly. You receive your API key immediately, and you can start sending requests right away. The process is designed to be frictionless, removing barriers to entry for developers.
