FreeLLMAPI: Stack Every Free LLM Tier Behind One OpenAI-Compatible API

TL;DR: FreeLLMAPI is a free, open-source, self-hosted LLM router (MIT-licensed, by tashfeenahmed) that aggregates the free tiers of 34 providers and 635 free model endpoints behind a single OpenAI-compatible /v1 API. Stacked together those tiers add up to roughly 7.4 billion free tokens a month. It routes to the best available model, falls over when one is rate-limited, and tracks per-key usage so you stay under every cap. One local install, one API key, no credit card. It has 26,000+ GitHub stars.

Every serious AI lab offers a free tier these days. Google gives you a few million tokens a month. Groq and Cerebras hand out free fast inference. Mistral, Cohere, NVIDIA, HuggingFace and a dozen others each give away a slice.

The catch is that on its own, every single tier is a toy. A few thousand requests a day burns out in an afternoon, and juggling them by hand is the real tax: thirty-four different SDKs, thirty-four different rate limits, thirty-four places a request can silently fail. That friction is exactly what FreeLLMAPI removes.

Think of it as a local exchange that sits between your code and every free model. You add keys for whichever providers you have, point any OpenAI-compatible client at http://localhost:3001/v1, and it handles the rest. The free tiers that were individually useless now add up to real working inference capacity.

What FreeLLMAPI Actually Does

Concretely, it is a self-hosted router with one job: make free-tier models usable at scale. The flow is:

1. You install it on your own machine or server (Docker one-liner, or a desktop app for macOS and Windows).
2. You paste in the API keys for the free providers you already have. Google AI Studio, Groq, Cerebras, Mistral, OpenRouter, Cohere, Cloudflare Workers AI, Zhipu, NVIDIA, HuggingFace and more, plus any custom OpenAI-compatible endpoint you run yourself.
3. It exposes one unified /v1 endpoint and keeps every key encrypted in a local SQLite database, decrypted only in memory per request.
4. You point any OpenAI SDK at it. Claude Code, Codex CLI, Aider, Cline, Roo Code, Cursor and a dozen other tools configure themselves with a single command.
5. For each request, the router picks the best available model, and if that provider is rate-limited it fails over to the next one with cooldowns and key rotation.
6. It tracks per-key usage against every free-tier cap, so you stay under the ceiling without thinking about it.

On top of that core loop, it serves the full OpenAI-style surface: chat completions, completions, responses, embeddings, image generation, video generation, audio speech and transcription. There is also an Anthropic-compatible /v1/messages endpoint so Claude Code runs against the same pool, and a native Gemini surface for the Gemini CLI.

The Big Number

The headline is the aggregate. According to the project README, stacking the free tiers from its 34 providers amounts to roughly 7.4 billion tokens of usable inference per month across 474 model families and 635 endpoints. That is the ceiling if you unlock every provider; your real number depends on which keys you actually add.

Rather than a single magic API key, FreeLLMAPI is an aggregator. You bring your own free-tier access, and it multiplies it.

Smart Routing and Failover

A router is only as good as its routing. FreeLLMAPI maintains live speed, capability and reliability scores per model, and ranks your configured providers as a fallback chain. When one returns a 429 or a 5xx, it retries the next in line, respecting cooldowns and rotating keys so a single exhausted account does not stall the whole request.

A few details worth knowing:

• The same model available on several providers collapses into one entry with strict failover within the group.
• You can define named fallback profiles, like a coding chain or a vision chain, and switch per request via auto:<profile>.
• Conversations stay pinned to one model for 30 minutes, with an optional compact handoff note if a mid-chat switch does happen.
• An optional "fusion" virtual model fans your prompt out to several diverse free models in parallel, then a judge model synthesizes one answer from the drafts.

Self-Updating Model Catalog

The free-tier landscape shifts weekly. Providers launch models, retire them, and change quotas without notice. FreeLLMAPI handles this by pulling a signed model catalog from freellmapi.co on its own schedule, so new models and quota changes arrive without a git pull.

There is a real trade-off baked into that. Free installs track a monthly snapshot, meaning a new model reaches them about 30 days after it lands in the live feed. Premium routers, at $19 a year, get new models the same day.

How FreeLLMAPI Compares

OpenRouter is the closest hosted equivalent, but it is a cloud service and you do not add your own free keys to it. LiteLLM is the closest open-source equivalent, but it expects you to write code and config to wire providers together. LM Studio and Ollama are excellent for local models but only speak one format. FreeLLMAPI sits in the sweet spot: it speaks OpenAI natively, aggregates third-party free tiers, and self-hosts.

Where FreeLLMAPI Falls Short (Honestly)

For all that it does, it is not a magic free API. The caveats are real:

• It is single-user and local-first by design. No team accounts, no shared deployment story. This is a tool for one person's machine, not an org-wide gateway.
• You bring your own keys. It aggregates free tiers, it does not conjure them. The 7.4-billion-token headline assumes you have signed up for a good share of the 34 providers. That is a decent chunk of setup work.
• Every provider cap still applies to you. FreeLLMAPI keeps you under the ceilings, but it cannot raise them. Heavy usage still hits real limits, just more gracefully.
• New models are 30 days slow on the free plan. If being first to a frontier release matters to you, the $19/year premium catalog is effectively part of the real product.
• The setup is technical. The Docker one-liner is friendly, but a Node-based router with 30+ possible providers is still a dev tool, not a grandma-friendly install.
• Some providers have geographic or account friction. ModelScope needs an Aliyun China account binding, and a few of the listed free tiers are aimed at specific regions. Your actual provider count will vary.

How to Get Started (5 Minutes)

The quickest path is the Docker one-liner, which sets up ~/freellmapi, generates an encryption key, and starts the container:

curl -fsSL https://freellmapi.co/install.sh | bash

The script is public at that URL if you want to read it before piping it to bash, and re-running it is safe: it preserves your .env and encryption key while updating the container.

Once it is up:

1. Open http://localhost:3001.
2. Add your free-tier provider keys on the Keys page.
3. Reorder the fallback chain to taste.
4. Grab your unified API key from the Keys page header, then point any OpenAI SDK at http://localhost:3001/v1.

On Windows the easiest route is the desktop .exe installer from the Releases page. Coding agents configure themselves with generators like npx freellmapi setup-claude or setup-codex, which back up existing config and never clobber it. The dashboard also ships in 60 languages, if that matters to you.

Who's Behind It

FreeLLMAPI is by tashfeenahmed, an active and fast-moving developer (commits are landing daily, and the project has 744 commits and 26,000+ GitHub stars in about five months). It is MIT-licensed under an open, multi-author project, with a real contributor base and a public Discord-friendly development style.

The Bottom Line

FreeLLMAPI turns the dozens of free-tier scraps scattered across the AI industry into something you can actually build on. If your bottleneck was the friction of wiring up and rate-limiting thirty providers by hand, this removes it: one endpoint, one key, sensible routing and failover, and your own machines and keys in control the whole time.

It is not a get-rich-free API and it will not outrun your free caps. But for a solo developer, a tinkerer, or anyone tired of paying for a GPT-4-class subscription when free frontier access is sitting unused across ten accounts, it is the single most useful free LLM tool to set up this year. If you have been looking for a free LLM API, a self-hosted alternative to OpenRouter, or a way to stop juggling free model keys, this is the one to try.

Looking for other free AI tools? Check out our coverage of OpenWorker for a free AI coworker that finishes tasks, or pxpipe for cutting Claude Code token costs in half.