Free LLM APIs: What You Can Use Now and Their Limits

Updated 10 November 2026 · Standard formula, shown below

Free LLM APIs let you send prompts to hosted language models without paying per request, usually through a limited free tier or trial credit. You can use them for prototypes and modest workloads, but quotas, model access, speed, and data terms vary—and “free” rarely means unlimited or guaranteed availability.

What counts as a free LLM API?

An API is a programmatic way to submit text or other supported inputs to a model and receive generated output. Unlike a free chat website, an API can be called from an application or script, so you can automate tasks such as summarizing documents, answering questions, or generating structured text.

Free access generally comes in one of three forms:

Ongoing free tier: A provider allows a limited amount of API use at no charge, often subject to rate limits or model restrictions.
Trial credits: A new account receives a balance that can be used for a limited time or until it runs out.
Local inference: You run an open-weight model on your own computer or server. There may be no provider charge, but you supply the hardware, electricity, and setup.

These options are not interchangeable. A model with open weights is not automatically available through a free hosted API, and a free chatbot subscription does not necessarily include API access.

Free hosted APIs available to check

Several established AI platforms have offered free or trial API access, but the details change frequently. Treat the provider’s current pricing and quota page as authoritative; availability may depend on your location, account status, model, and whether the offer is a trial or continuing tier.

Google AI Studio and Gemini API: Google has provided a free tier for selected Gemini API models, with model-specific request and usage limits. The free tier is separate from paid usage, and availability and data-use terms can differ by tier.
GroqCloud: Groq has offered developer access to selected models with rate limits. Its model catalog and quotas can change; check current limits before designing an application around a particular model.
Hugging Face Inference Providers: Hugging Face has offered limited credits or access for inference, with terms that depend on the provider and plan. A model listed in its catalog is not proof that unlimited free API calls are available.
Cloud-provider trials: Some cloud platforms provide introductory credits that may cover eligible model services. These are temporary credits, not a permanent free quota, and may require billing details.

There is no stable universal list: providers revise offers, retire models, change eligibility, or introduce new tiers. Before building around one, confirm the exact model identifier, free allowance, expiration rules, and whether a payment method is required.

What the limits usually mean

“Free” access is commonly bounded by several independent controls. A request can fail even if you have not used all of an advertised monthly allowance, for example, because you have reached a per-minute limit.

Request and token quotas: Providers may cap calls per minute or day, or cap input and output tokens. Tokens are pieces of text, not exactly words; long prompts and long answers consume more.
Model selection: The free tier may cover only certain models, smaller models, or older versions. Premium capabilities may require a paid plan.
Context and output limits: A model may reject a prompt that exceeds its context window or stop generating at a maximum output length.
Capacity and latency: Free traffic may be throttled, queued, or temporarily unavailable at busy times. A free tier is not necessarily a service-level guarantee.
Trial expiration: Credits can expire, run out, or apply only to specified services. Once exhausted, calls may stop or become billable if billing is enabled.

Check whether limits are per account, project, key, or model. Also look for what happens at the limit: a clear error, a cooldown, a fallback model, or continued paid billing.

How to choose one for a project

Start by estimating what the application needs rather than choosing on the word “free.” A small experiment that sends a few short prompts has different requirements from a public-facing tool with unpredictable traffic.

Define the task. Note whether you need text only, image input, tool calling, structured output, or a large context window. Support varies by model.
Estimate usage. Approximate calls per day and prompt and response length. Compare those figures with both request-rate and token limits.
Test output quality. Try representative prompts, including difficult cases, and check accuracy, consistency, and formatting. A free model may be adequate for drafts but not for an unsupervised high-stakes decision.
Review terms and privacy. Determine how prompts and outputs are handled, whether they may be retained or used to improve services, and what controls apply to your account tier.
Plan for a limit or outage. Add sensible retries for temporary rate limits, a user-facing fallback, and a way to switch providers or models if the service changes.

Keep API keys on a server or in a secret manager, not in browser code or a public repository. A leaked key can be used by someone else and may create charges if the account permits paid use.

Free APIs versus local models

A hosted API avoids installing model software and managing inference hardware. It is usually the easiest route for a quick prototype, and the provider handles serving the model. The trade-offs are quotas, dependence on an external service, network latency, and the need to assess the provider’s data practices.

Local models avoid sending prompts to a hosted API when run entirely on your own machine, and they do not have a provider’s per-call API bill. But they are not costless: capable models can require substantial memory and processing power, and setup, updates, and performance tuning are your responsibility. A small model may also produce weaker results than a larger hosted model. “Open weights” describes access to model parameters, not a promise that the model is free of license conditions or operational costs.

Common problems and safety checks

Quota errors are often mistaken for a broken API. Check the response status and provider dashboard, confirm the key belongs to the expected project, and distinguish a requests-per-minute limit from a token or credit cap. If you receive a temporary rate-limit response, wait before retrying and use backoff rather than sending a rapid stream of identical requests.

Do not put confidential or personal information into a service until you have checked its current data terms and your organization’s requirements. Model responses can be incorrect, incomplete, or fabricated; verify factual claims and avoid treating generated output as a substitute for qualified review in consequential settings. Also test for prompt injection when a model processes untrusted documents or web content, especially if the application gives it access to tools or private data.

Frequently Asked Questions

Are free LLM APIs really free?
Some provide a continuing no-charge allowance, while others offer only trial credits. Limits, eligibility, and billing behavior differ, so verify the current terms and disable paid usage if you do not want the possibility of charges.

Can I use a free API in a commercial app?
Possibly, but free access alone does not grant every use right. Check the API terms, model license where applicable, rate limits, and any rules for commercial use before launch.

Do free chatbot accounts include API access?
Not necessarily. A consumer chat product and a developer API are usually separate services with separate accounts, quotas, and billing rules.

What is the best free LLM API?
There is no best choice for every task. Compare current free quotas, supported models, output quality, latency, data terms, and the features your application needs.

Can I avoid all usage limits by switching to an open model?
Not automatically. A hosted service for an open-weight model still has provider limits; running it yourself avoids that provider quota but is limited by your hardware and operating capacity.

Method: the standard formula for this calculation, rounded as each result states. See how the calculators work or report an error.