What LLM API Is Free? Options, Limits and a Real Trial

The short answer: three kinds of free

When people ask what llm api is free, they usually mean one of three different things, and the differences matter more than the price tag.

• A free tier from a hosted provider. You get a capped amount of usage, often with tight rate limits, and the terms can change without much notice.
• A model you run yourself. The weights of many open models cost nothing to download. The GPU, the electricity and your setup time do not.
• Trial credit. A small prepaid balance that lets you test the real service with real requests before you put money in.

None of these is a permanent free API for a product with users. If you only need to try a model, write a prototype or check whether an endpoint works with your client, any of the three can do the job. If you are building something that people will use every day, plan for a paid path from the start, because the moment a free tier ends is usually the moment your project is busiest.

What free tiers usually look like

Free tiers differ between providers and change often, so treat this section as a checklist of things to look for, not as a statement about any one company. For current numbers, check their current pricing and terms pages.

• Request caps. Free usage is normally limited per minute and per day. A chat app with a handful of users can hit those caps in an afternoon.
• Model choice. Free access may cover only smaller or older models, or a queue with lower priority.
• Data terms. Some free offers allow the provider to use your prompts to improve their products. That is fine for a test with public text. It is a poor fit for private documents, customer messages or personal fiction.
• Content rules. Mainstream providers apply their own safety filters. If your use is lawful but adult in tone, such as fiction, roleplay or a companion app for adults, a filtered model will refuse often, and refusals still eat into your free quota.

The content point is the one that surprises people. A free request that returns a refusal is not free in any useful sense, because you spent your time and part of your cap on nothing. If an unfiltered model is what you need, filter the list of free options by that first.

Self-hosting an open-weight model

Running a model on your own machine is the closest thing to truly free, with a catch: you pay in hardware and attention. The weights are often free to download, though each one has its own license, so read it if you plan to build a product on it.

What you actually spend:

• Memory. A model has to fit in GPU memory, or in system memory if you accept much slower output. Larger models need more of it, and shrinking a model by quantizing it trades some quality for space.
• Setup time. You install a runtime, pick a quantization, tune the context size and keep it all updated.
• Power and noise. A GPU under load draws real electricity, and a laptop will tell you about it.
• Concurrency. One GPU serves one or two requests comfortably. Serving ten users at once is a different project.

For learning, offline work and privacy by default, local is excellent. For a small app that must answer from any device at any hour, many developers end up renting capacity instead. A hosted API turns the fixed cost of hardware into a cost per token, which is cheaper when your usage is small and uneven.

Shared keys and community proxies

Search for a free API and you will find lists of public endpoints, shared keys and proxies run by hobbyists. Treat these with suspicion. A shared key can be revoked in the middle of a demo. The person running a proxy can read every prompt that passes through it. There is no support, no stated limits and often no way to tell which model is answering you.

If your prompts contain anything you would not paste into a public forum, skip this category entirely. A small paid balance with a clear data policy is a better deal than a free endpoint whose owner you cannot identify.

What the trial on this site gives you

This service is a hosted, OpenAI-compatible text API with one model, uncensored, tuned to answer without content refusals for lawful adult use. It does not have a permanent free tier. It has a trial: after you sign up with Google, or with an email and password, the API key page shows a button that claims $0.50 of trial credit. It takes one click, needs no payment details, is valid for 7 days and is limited to one per person.

A trial key runs at 2 concurrent requests and 60 requests per minute, which is plenty for testing a client or a prompt. Pricing is $0.15 per 1M input tokens and $0.50 per 1M output tokens. Here is what $0.50 buys for a few typical request sizes, computed exactly:

Request size	Cost per request	Requests for $0.50
500 tokens in, 200 out (short chat turn)	$0.000075 + $0.000100 = $0.000175	about 2,857
2,000 in, 500 out (chat with some history)	$0.000300 + $0.000250 = $0.000550	about 909
20,000 in, 1,000 out (long scene or document)	$0.003000 + $0.000500 = $0.003500	about 142
90,000 in, 2,000 out (near the 100,000 window)	$0.013500 + $0.001000 = $0.014500	about 34

So the trial is enough to run a few hundred realistic requests, test streaming, try JSON mode and tool calling, and see how the model behaves with your prompts. It is not meant to run a product. For the exact setup steps, the quickstart in the docs has working code for the OpenAI SDK, Node and curl.

When prepaid credit makes sense

Once the trial is spent, the model is pay as you go from prepaid credit. There is no subscription. You top up any whole amount from $20 to $500, and the credit never expires, so a balance you do not use this month is still there next month. Failed or refused requests are not charged. Larger top-ups carry a bonus: +5% from $50, +10% from $100 and +15% from $200.

The bonuses work out like this:

• $20 buys $20.00 of credit.
• $50 buys $52.50 (a 5% bonus of $2.50).
• $100 buys $110.00 (a 10% bonus of $10).
• $200 buys $230.00 (a 15% bonus of $30).

This site accepts crypto top-ups only: USDT on TRC20 or USDC on Base. If you do not hold either, that is a real hurdle and worth knowing before you start. The pricing page has the current details.

To see what a $20 top-up means in practice, use the same request sizes as above. At 500 tokens in and 200 out, $20 covers about 114,000 requests. At 2,000 in and 500 out, it covers about 36,000. A hobby chat app with a few users rarely spends that in a month, which is why the minimum is workable even for small projects.

How to test any free option before you rely on it

Whichever route you pick, spend one afternoon on a proper test instead of a single hello-world call.

1. Use your real prompts. A model that writes a nice haiku can still fail on your system prompt, your language or your output format.
2. Measure refusals. Send twenty prompts from your actual use case and count how many come back refused or watered down.
3. Trigger the limits on purpose. Send requests in parallel and see what the error looks like. Here, going over the per-minute limit returns HTTP 429 with the code rate_limited, and too many simultaneous requests returns 429 with concurrency. A temporary overload also returns 429 with a Retry-After: 5 header, so your client should wait and retry.
4. Read the data terms. Prompts here are not used for training. For any other service, find the statement in writing.
5. Estimate the paid cost. Take your average tokens per request, multiply by expected requests, and compare. The arithmetic takes five minutes and prevents most surprises.

The service is text only. There is no image, audio or video generation, no embeddings and no fine-tuning, and there is a hard limit: no sexual content involving minors. If your project needs any of those missing pieces, a free option elsewhere will not fix that, so settle it first. The page on the OpenAI-compatible text model explains what the endpoint does and does not cover.

More guides

• Best Local LLM for Roleplaying, or a Hosted API?
• NSFW LLM API in Python: Streaming, JSON and Tools
• Uncensored LLM API: OpenAI Compatible Text Model