Are There Any LLM With a Free API? What Free Costs

The short answer

If you are asking are there any llm with free api access, the honest answer has three parts. Some providers publish free tiers with tight rate limits. Some open-weight models can be run on your own hardware, which costs electricity and a GPU instead of an invoice. And some hosted APIs, including this one, give you a small trial balance so you can test before you pay.

This service does not have a free unlimited tier. What you get is a one-time trial of $0.50 in credit, claimed with one click on the API key page after sign-up. It needs no card, it is valid for 7 days, and it is limited to one per person. After that you top up and pay per token. The rest of this article puts numbers on both stages so you can judge whether $0.50 is enough for your test and what a month of real use would cost.

What the trial gives you

The trial is meant for one job: letting you confirm the API behaves the way you need before you spend anything. You sign up with Google, or with an email and password, open the API key page, and claim the credit. The key works with the same endpoint and the same model ID (uncensored) as a paid key.

Two differences apply to a trial key. It is allowed 2 concurrent requests and 60 requests per minute, where a full key gets 8 concurrent and 300 per minute. And the balance expires after 7 days. If you are wiring up a client, a script or a small bot, those limits are plenty. If you plan to load test with parallel workers, you will hit HTTP 429 with the concurrency error code quickly, which is a signal to top up rather than a bug.

Failed or refused requests are not charged, so a request that errors out does not eat your trial balance. Each response also carries an X-Balance-USD header, which lets you watch the balance fall as you test.

How far $0.50 goes: worked token math

Prices are $0.15 per 1M input tokens and $0.50 per 1M output tokens. Divide by a million and you get the cost per token: $0.00000015 for input and $0.0000005 for output. Output costs a little over three times as much as input, so long answers move the bill more than long prompts.

A short chat message

Take a request with 1,000 input tokens (a system prompt plus a few turns) and 500 output tokens.

• Input: 1,000 x $0.15 / 1,000,000 = $0.00015
• Output: 500 x $0.50 / 1,000,000 = $0.00025
• Total: $0.0004 per request

$0.50 divided by $0.0004 is 1,250 requests. That is a full afternoon of manual testing, or a small script run many times.

A heavier request

Now 4,000 input tokens and 1,000 output tokens, which is a long conversation history with a detailed reply.

• Input: 4,000 x $0.15 / 1,000,000 = $0.0006
• Output: 1,000 x $0.50 / 1,000,000 = $0.0005
• Total: $0.0011 per request

$0.50 divided by $0.0011 is about 454 requests.

Filling the whole context window

The context window is 100,000 tokens, prompt plus completion. A request with 98,000 tokens of input and 2,000 of output costs 98,000 x $0.15 / 1,000,000 = $0.0147 for input plus 2,000 x $0.50 / 1,000,000 = $0.001 for output, so $0.0157 in total. The trial covers about 31 of those. Long-context work is where a trial balance disappears fastest, so test it with a few requests rather than a loop.

What happens after the trial

When the $0.50 is gone or the 7 days are up, you move to prepaid credit. There is no subscription and no monthly minimum. You add credit once, and requests draw it down at the per-token prices above. Credit never expires, so a top-up you use slowly does not decay.

Top-ups are any whole amount from $20 to $500. Larger ones add a bonus on top: 5% from $50, 10% from $100 and 15% from $200. In practice that looks like this:

You pay	Bonus	Credit you get
$20	none	$20.00
$50	+5%	$52.50
$100	+10%	$110.00
$200	+15%	$230.00

You can pay by card (Visa, Mastercard, Apple Pay, Google Pay) or by crypto (USDT on TRC20, USDC on Base). The pricing page has the current top-up details.

What a real month looks like

A trial tells you the API works. The question that decides whether you keep using it is what steady use costs. Here are two sketches, both computed from the same prices.

A small companion or chat bot. Say 10,000 messages in a month, each with 1,500 input tokens and 400 output tokens. Input is 10,000 x 1,500 = 15,000,000 tokens, which is 15 x $0.15 = $2.25. Output is 10,000 x 400 = 4,000,000 tokens, which is 4 x $0.50 = $2.00. The month costs $4.25, so a $20 top-up covers a bit under five months at that pace.

A batch job. Say you process 2,000 documents, each 3,000 tokens in and 600 tokens out. Input is 6,000,000 tokens, or $0.90. Output is 1,200,000 tokens, or $0.60. The whole batch costs $1.50. The same job on a slow afternoon of your own testing costs a few cents, which is why the trial is usually enough to decide.

These are examples, not promises about your workload. Your own numbers depend on prompt length, how much history you resend and how long the replies run. The usage field in each response gives the exact token counts, so you can multiply it out yourself after a day of use and project the month.

Other ways to get an LLM without paying

If $0.50 is not enough and you do not want to pay yet, there are other routes, each with a cost that is not on an invoice.

• Free tiers from other providers. Several providers offer limited free access. The limits, the models and the terms change often, so check their current pricing and rules before you build on one.
• Running a model yourself. Open-weight models can run locally with tools such as Ollama or llama.cpp. You pay in hardware and setup time, and bigger models need a serious GPU. A local model is a good fit when privacy or zero marginal cost matters more than speed.
• Self-hosting behind a proxy. If you already run a gateway such as LiteLLM, you can point it at an OpenAI-compatible endpoint. The LiteLLM comparison covers when a gateway helps and when a direct connection is simpler.

Free access has trade-offs that are easy to miss: a free tier may refuse content a lawful adult use case needs, may cap you at a few requests per minute, or may change terms without notice. If you need an endpoint that answers without content refusals, is OpenAI-compatible and has stable pricing, a small prepaid balance is often the cheaper path once you count your own time. The one hard limit on this service is no sexual content involving minors.

Using the trial credit well

A few habits stretch $0.50 further and give you a more honest test.

1. Set max_tokens while testing so a runaway reply cannot spend more than you planned. It is optional in production.
2. Keep prompts short until the behavior is right, then test with your real prompt length once.
3. Use streaming for interactive tests. Streaming returns usage at the end, so you still see the token counts.
4. Check the X-Balance-USD header after a run to confirm the spend matches your arithmetic.

This request spends a fraction of a cent and shows you the response shape and headers:

Copycurl -i https://api.llmapisource.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "uncensored",
    "messages": [{"role": "user", "content": "Say hello in one sentence."}],
    "max_tokens": 50
  }'

The same call works from any OpenAI SDK once the base URL and key are changed. The quickstart shows the Python and Node versions, including streaming and tool calling.

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• LiteLLM vs Direct Uncensored LLM API