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We re-checked every major free LLM API on 26 July 2026. Three of the five have quietly stopped publishing their rate limits. Here is what is still verifiable.
The honest answer in 2026 is that you can no longer choose a free OpenRouter alternative from a comparison table, because most of the table has been taken down. We re-checked all five major free LLM APIs against their own official documentation on 26 July 2026. Only one of them still publishes its free-tier rate limits. That single fact should change how you build.
Most "best free LLM API" posts print a tidy grid of requests-per-minute numbers. We went to each provider's own docs to refresh ours and could not rebuild that grid honestly. Here is the state of published limits, with the source we checked:
An earlier version of this article quoted "Cerebras: ~30 RPM, 1M tokens/day" and "Mistral: 1B tokens/month". We have removed both, because neither is verifiable against the providers' current documentation. If you find those numbers elsewhere, check the publication date before you plan capacity around them.
Groq remains the outlier that publishes everything, so it is the only provider whose free tier you can plan against without logging in. Figures below are verbatim from Groq's docs on 26 July 2026:
| Model | RPM | RPD | TPM | TPD |
|---|---|---|---|---|
| llama-3.1-8b-instant | 30 | 14,400 | 6,000 | 500,000 |
| llama-3.3-70b-versatile | 30 | 1,000 | 12,000 | 100,000 |
| openai/gpt-oss-120b | 30 | 1,000 | 8,000 | 200,000 |
| openai/gpt-oss-20b | 30 | 1,000 | 8,000 | 200,000 |
| qwen/qwen3.6-27b | 30 | 1,000 | 8,000 | 200,000 |
| groq/compound | 30 | 250 | 70,000 | — |
| whisper-large-v3 | 20 | 2,000 | — | — |
Read the second row carefully, because it is the trap almost everyone hits. Moving from the 8B model to the 70B model costs you 14x your daily request budget — 14,400 requests per day collapses to 1,000. A prototype that felt limitless on llama-3.1-8b-instant will die within an hour of being switched to the bigger model. Capacity on free tiers is a property of the model, not the account.
Google, Cerebras and Mistral all moved to per-account limits visible only after login. Nobody has explained why, and the reason matters less than the consequence: your free-tier capacity is now a runtime property, not a design-time constant. It can differ between two accounts on the same provider, and it can be changed without a changelog entry you would ever see.
You cannot design around a number you are not allowed to read. So stop trying. The only architecture that survives an unpublished, silently-changing quota is one that treats a 429 as normal and routes around it.
Every provider listed above speaks an OpenAI-compatible chat-completions API. That compatibility is the whole opportunity: the request shape is identical, so the only things that differ are the base URL, the model identifier, and how each one fails. A pooling client absorbs those three differences and gives your application a single endpoint that stays up when any individual free tier does not.
That is what we built freelm for. It is open source, runs in Python and Node.js, and pools OpenRouter, Gemini, NVIDIA NIM, Groq, Cerebras and Mistral behind one call:
pip install freelmThree behaviours matter more than the provider list. Automatic failover means a 429 or a 5xx from one provider transparently retries on the next instead of surfacing to your user. Circuit breakers stop hammering a provider that is already refusing you, which is what turns a soft rate limit into a hard ban. Dynamic model discovery re-reads each provider's currently-free model list at runtime, so the free model that quietly disappeared last week does not become a production incident this week — which, given everything above, is the feature that ages best.
Full setup, configuration and provider-specific notes are in the freelm documentation.
No — and this is where most "alternatives" posts overreach. OpenRouter's breadth of model choice behind one key is genuinely useful and nothing here replaces it. The failure mode being solved is not that OpenRouter is bad; it is that any single free tier is a single point of failure. Keep OpenRouter as one provider in the pool and you keep its catalogue while removing its outage.
Pooling raises your effective ceiling; it does not make free tiers production infrastructure. Do not use them when you have a latency SLA, because free-tier queues are deprioritised by design and no provider owes you a response time. Do not use them for data you are not willing to send to six vendors' separate terms of service — pooling multiplies your data-handling surface, and that is a real cost, not a footnote. And do not use them where a 429 becomes a customer-facing outage.
Free tiers are excellent for prototypes, side projects, batch and offline jobs, evaluation harnesses, and personal tools. Revenue-generating traffic belongs on a paid tier. The honest version of this article ends there rather than pretending otherwise.
Every number above is either linked to the provider's own documentation or explicitly marked as unpublished. Check the four source links in the first section before planning capacity, and check them again in a month — the entire point of this article is that they move. If you find a discrepancy, the provider's docs win over this page.
Q: What is the best free alternative to OpenRouter?
A: There is no single best one, because the free tiers are no longer comparable on paper. As of 26 July 2026 only Groq still publishes a per-model free-tier rate-limit table; Google, Cerebras and Mistral have all moved their numbers behind a console login. The practical answer is to run two or three providers behind one OpenAI-compatible client and fail over between them, which is what freelm does.
Q: Is OpenRouter's free tier still usable in 2026?
A: Yes, and you do not have to give it up. OpenRouter still exposes free model variants and remains useful for breadth of model choice. The problem is that it is a single point of failure: when its free pool is saturated your app stops. Treat OpenRouter as one provider in a pool rather than as the pool itself.
Q: Which free LLM API has the highest published rate limit?
A: Of the providers that still publish numbers, Groq's llama-3.1-8b-instant is the most generous at 30 requests per minute, 14,400 requests per day, 6,000 tokens per minute and 500,000 tokens per day. Its larger models are far tighter — llama-3.3-70b-versatile drops to 1,000 requests and 100,000 tokens per day.
Q: Why did Google stop publishing Gemini free-tier limits?
A: Google has not given a reason. The rate-limits page, last updated 21 July 2026, now says limits depend on your usage tier and directs you to view them in Google AI Studio instead of listing them. Practically this means any blog post quoting a specific Gemini free-tier RPM number is quoting something it cannot verify.
Q: Do these providers need a credit card?
A: It varies by provider and it changes, so verify at signup rather than trusting a listicle. What is stable is the shape of the risk: free tiers are evaluation tiers, and every provider reserves the right to change or withdraw them without notice.
Q: Can I use these providers without rewriting my OpenAI code?
A: Yes. Groq, Cerebras, Gemini, NVIDIA NIM, Mistral and OpenRouter all expose OpenAI-compatible chat-completions endpoints, so the request and response shapes match. What differs is the base URL, the model identifiers and the failure modes — which is the part a pooling client exists to absorb.
Q: When should I not use free LLM tiers?
A: Anything with a latency SLA, anything handling data you cannot send to six different vendors' terms of service, and anything where a 429 is a customer-facing outage. Free tiers are for prototypes, side projects, batch jobs and evaluation. Production traffic that earns money should sit on a paid tier.
Three of the five major free LLM APIs stopped publishing their rate limits, which makes provider-by-provider comparison a dead end and failover the only durable strategy. Pool them behind one OpenAI-compatible call with freelm, open source from Shahriar Labs, and read the docs to get running in a few minutes.
Limits verified against provider documentation on 26 July 2026 by Shihab Shahriar Antor, founder of Shahriar Labs. Providers change free tiers without notice — always confirm against the linked official pages.
Beyond chatbots. How Shahriar Labs orchestrates multi-agent systems to solve complex engineering problems autonomously.
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