**FreeLLM API: 34 providers and 635 free LLM endpoints behind a single /v1 — 31,000 stars in one month**

FreeLLM API: 34 providers and 635 free LLM endpoints behind a single /v1 — 31,000 stars in one month

🔎 One endpoint, 34 providers, zero bills

The pitch fits in three sentences, and it sent the counters soaring: "7.4 billion tokens per month. 34 free LLM providers. 635 free model endpoints." That's the pitch of FreeLLMAPI, the tashfeenahmed/freellmapi repo that has stacked up more than 31,000 stars in one month and has been sitting atop GitHub's trending charts for several weeks (October 2026).

The technical promise fits on a single line: a server you run on your own machine, which exposes a /v1 endpoint that is rigorously OpenAI-compatible, and routes every request to one of the 34 free providers it aggregates. Chat, embeddings, image, audio — everything goes through the same pipe, with intelligent routing, automatic failover, and encrypted keys.

Why now? Because anyone who builds agents knows it: the real wall is no longer technical, it's financial. And because free tiers have never been so numerous — or so scattered. But "free" comes with asterisks — we detail every one of them, with the figures from the official README, the Better Stack community guide, and feedback from early users.

The Essentials

• A single OpenAI-compatible /v1 endpoint: point any OpenAI client library at your server, routing is automatic. Chat, embeddings, image, and audio all included.
• 34 providers, 635 endpoints, 474 model families: from small-and-fast to reasonably capable, with automatic failover between providers (official README, October 2026).
• 7.4 billion tokens/month = combined capacity across all supported free tiers — not your personal quota. The most misunderstood point about the project.
• Self-hosted and encrypted: your keys stay with you, encrypted at rest; any OpenAI-compatible custom endpoint plugs in alongside the 34 built-in ones.
• For experimenting: yes. For production: no. Variable quotas, uneven reliability, no SLA — and the project doesn't pretend otherwise.

Recommended Tools

What FreeLLMAPI Actually Does

A server you spin up locally or on a VPS, which speaks the OpenAI dialect and reroutes each request to the best-positioned free provider at call time. That's it — and it's exactly what was missing.

On the code side, the change boils down to one line: the base URL of your OpenAI client points to your server instead of the official API. Your scripts, your agent frameworks, your notebooks keep believing they're talking to OpenAI. Behind the scenes, FreeLLMAPI picks the provider, manages quotas, and absorbs errors. The community confirmed it from the very first weeks: it really is a self-hosted router that combines free tiers into a single OpenAI-compatible API.

The 635 endpoints cover 474 model families, ranging from small-and-fast — short responses, minimal latency, perfect for tool loops — to reasonably capable, suited for reasoning, code, and writing.

Routing and Failover: The Real Feat

The routing layer is the product. A provider rate-limits you? The request gets sent to the next one without your code noticing any difference. A model disappears from a free tier? The router routes around it. For an agent chaining together dozens of calls, this resilience changes everything: an overnight run no longer dies on a 429.

Second strength: extensibility. Any OpenAI-compatible custom endpoint — a local model server, a company API, a niche provider — plugs in alongside the 34 built-in ones. FreeLLMAPI then becomes the single layer between your code and all your models, free or not.

Third strength, often overlooked: encryption of keys at rest. You're handling dozens of API keys here; the fact that they don't sit in plaintext in a config file is a detail that matters.

Beyond Chat: Embeddings, Images, Audio

The same /v1 serves chat, embeddings, image generation, and audio. For a RAG pipeline, that's the detail that matters: embeddings and generation go through the same configuration, without multiplying SDKs or authentication formats.

7.4 billion tokens per month: what the number really means

It's the combined capacity of all supported free tiers, not a promise made to each individual user. A video analysis of the project says so bluntly, and the README itself, for that matter, presents it as a "combined" capacity. Rare marketing honesty — let's salute it.

The math is simple: 34 providers whose published caps add up, which works out to an average of over 200 million tokens per provider per month. But your actual cap, yours specifically, is the sum of the quotas of the providers you actually connect with your own keys. No more, no less.

Concretely, for a solo developer hooking up the main free tiers, we're talking thousands to tens of thousands of requests per day depending on the models. Huge for experimenting, prototyping, and running personal agents. Negligible, on the other hand, for serving the traffic of a public application — that's not what the project is for anyway.

So the number is marketing, but it describes a real phenomenon: the sum of the providers' scattered generosity, once aggregated and orchestrated, becomes a serious resource. That's what the whole niche is built on — the GitHub topic free-llm-api lists several projects of the same kind, discussed further below.

Why It's Exploding Now

Because the cost of experimenting with agents has become the number one blocker for indie developers, and the fragmentation of free tiers has never been more punishing.

An agent burns through tokens: planning, tool calls, error recovery, evaluations, prompt testing. A single debugging session on a frontier model — GPT-5.5 (91 on the aggregate index) or Claude Opus 4.7 (90) — can cost more than a month's subscription. Open models, for their part, have closed much of the gap: DeepSeek V4 Pro sits at 88, Kimi K2.6 at 84. But every free tier has its own SDK, its own quota format, its own error codes. Managing five providers by hand is a part-time job.

FreeLLMAPI attacks exactly this pain point. And the niche is already active — proof that the pain is real: FreeAIGateway aggregates 16+ providers for ~1.7 billion tokens/month with a Claude-compatible endpoint; freelm covers Gemini, Groq, OpenRouter, Cloudflare, Mistral, NVIDIA NIM, Z.ai and Cohere with failover and key rotation; awesome-free-llm-apis catalogs the rest. FreeLLMAPI positions itself at the top for aggregated volume, with a 4x factor on paper.

The trend isn't new. Our comparisons of the Best Free LLMs (May 2026) and June 2026 already showed free tiers getting more generous month after month, while open models climb the rankings. FreeLLMAPI is the orchestration layer this picture was missing. It also fits into a broader open source moment: OpenCV 5.0 now integrates LLMs and VLMs natively, and open source AI is moving from being an alternative to being infrastructure.

Installation: ten minutes and your own keys

git clone, configuration, launch: the Better Stack community guide details every step, and the ten-minute promise is broadly kept if you already have your keys.

git clone https://github.com/tashfeenahmed/freellmapi.git
cd freellmapi

Then, three moves. One: grab your free API keys from the providers you want to aggregate — that's the longest part, each signup takes a few minutes. Two: enter them in the configuration, where they are encrypted at rest. Three: launch the server. Better Stack sums it up honestly: the project is self-hosted, the aggregation covers roughly 28 to 34 providers depending on your configuration, and both quotas and reliability depend on each provider.

On the client side, a single environment change:

export OPENAI_BASE_URL="http://localhost:8000/v1"

(Check the exact port in the repo's documentation.) For a server that runs 24/7 without monopolizing your dev machine, an entry-level VPS from Hostinger does the job — the router only forwards requests, inference stays with the providers, so the machine load is minimal.

Last piece of advice: set up two or three major providers first, validate routing and failover with a few calls, then expand. Hooking up all 34 at once is the best way to lose track of who answers what when behavior goes off the rails.

FreeLLMAPI vs OpenRouter vs direct free tiers

FreeLLMAPI applies OpenRouter's mechanics to free access — but you take on the operational burden that a hosted service carries for you.

My take: these are complementary tools more than competitors. FreeLLMAPI for iterating for free, OpenRouter or the official APIs when the project becomes serious. The frontier — GPT-5.5, Gemini 3.1 Pro, Claude Opus 4.7 — remains on the paid side anyway, whichever tool you use. To choose your models at each step, our monthly comparison of the best LLMs and its October 2026 edition remain the go-to references.

For anyone coding agents, the cost of experimenting drops to zero

This is where the project truly changes the game: agents are the biggest token consumers, and the biggest beneficiaries of failover.

An agent loop means ten, fifty, a hundred calls for a single finished task — plan, call a tool, correct, start over. Add retries, evaluations, and prompt testing, and iteration becomes a luxury. With FreeLLMAPI, your exploration loops run on the free endpoints, and you only pay if you deliberately plug a frontier model into a critical step. The cost of making mistakes drops to zero, so you test ten times more ideas.

Second benefit, often underestimated: the resilience of long runs. An agent working overnight crosses rate limits without stopping — it hops from provider to provider. For a job scheduled at 2 a.m., that's the difference between waking up to a result and waking up to a traceback.

The zero-cost RAG pipeline

The loop is closed on the data side: combine it with Crawl4AI, GitHub's number 1 open source crawler for feeding agents and pipelines, and you get a complete RAG pipeline — collection, embeddings, generation — at zero token cost. Free embeddings via the same /v1 make RAG experimentation truly free, not just "almost free".

To go further: our feature on free AI APIs details the quotas provider by provider, and if you want to break out of those quotas by running on your own hardware, head over to the best local LLMs.

The Limitations, Explained Honestly

FreeLLMAPI is an excellent aggregation layer for experimentation. It is neither a service contract nor an infinite-token machine — and the project makes no claim to the contrary.

Quotas remain individual. Each provider enforces its own per-minute and daily limits. The routing maximizes the use of what you connect; it doesn't create capacity. The 7.4 billion remains a collective ceiling, never a personal floor.

Reliability is uneven. Free tiers get deprioritized at peak hours, models disappear without notice, and latencies vary from one to five times depending on the hour. Better Stack documents this in black and white: quotas and reliability depend on each provider.

Frontier models remain paid. Google, OpenAI, and Anthropic keep their flagships — Gemini 3.1 Pro, GPT-5.5, Claude Opus 4.7 — out of the free tiers. The 635 free endpoints cover open weights and entry-level models, not the top of the leaderboard.

Compliance is your responsibility. The project aggregates free tiers through your own keys: read each provider's terms, respect their quotas, and don't use the tool to circumvent usage limits. For experimentation and personal use, that's exactly the intended framework.

The business angle, to wrap up. FreeLLMAPI's value isn't in the tokens — it's in the layer: routing, failover, OpenAI compatibility, encryption, extensibility. That's precisely what OpenRouter monetizes on the paid side. The free offering proves the demand here; the natural monetization for this kind of layer will remain multi-provider routing, never the resale of free tokens.

❌ Common Mistakes

Mistake 1: believing the 7.4 billion tokens are "for you"

The figure aggregates the capacity of all supported free tiers. Your actual ceiling = the sum of the quotas of the providers you connect with your own keys. The solution: list your providers, add up their published quotas, and size your ambitions accordingly — and keep some headroom, since caps change without notice.

Mistake 2: putting a production product on it

No SLA, unpredictable quotas, models pulled overnight: a service with real users can't rely on this. The solution: prototype and test with FreeLLMAPI, migrate real traffic to official APIs or OpenRouter — and optionally keep FreeLLMAPI as a secondary fallback.

Mistake 3: expecting GPT-5.5 and Claude Opus 4.7 for free and unlimited

Free tiers cover open weights and entry-level models, not proprietary flagships. The solution: tier your tasks — free models for the routine 90% (sorting, extraction, drafts, routing), a paid frontier model for the 10% that truly deserve it.

Mistake 4: exposing the server without precautions

Encrypted keys don't mean a public server. An OpenAI-compatible endpoint open to the Internet means your quotas being consumed by strangers. The solution: stay local or behind a VPN, enable server authentication, and monitor consumption per provider to catch any drift.

❓ Frequently Asked Questions

Is FreeLLMAPI really free?

Yes, on two counts: the software is open source, and the tokens come from the free tiers of the providers you connect with your own keys. The limits are those of the providers — quotas, rate limits, availability. The project itself costs nothing, apart from any VPS that hosts it.

How does it differ from OpenRouter?

OpenRouter is a hosted service: a single endpoint, pay-as-you-go billing, limited free routes. FreeLLMAPI is its self-hosted equivalent applied to free usage: your keys, your server, zero middleman — but you handle the installation and maintenance. One you buy, the other you host.

Do you need powerful hardware to host it?

No. The server routes requests, it doesn't run any models: an entry-level VPS or your desktop machine is more than enough. Inference runs on the providers' side. The load depends on your request volume, not on the size of the models being routed.

Can it be used in production?

For a low-traffic personal project, possibly, provided you handle the monitoring yourself. For a product with real users: no — unpredictable quotas, no SLA, variable reliability. The sensible sequence: prototype for free, go to production on official or paid APIs.

How many requests can you make per day?

It depends on the connected providers and their current quotas. By plugging in the main free tiers, a solo developer typically gets enough to run thousands of daily requests — plenty for prototyping an agent, not for powering a public application.

Are my API keys safe?

They are encrypted at rest by the project, and the server runs on your side — nothing passes through a third party. The real risk surface is network exposure: keep the server local or behind a VPN, and never expose the endpoint to the Internet without authentication.

✅ Conclusion

FreeLLMAPI doesn't make LLMs free — it finally makes their freeness usable, by turning 34 scattered quota islands into a single endpoint your code already knows. Install it, plug in your keys, iterate at zero cost — and keep in mind that it's a fantastic proving ground, not a production foundation. To pick the models that will earn your future budget, our roundup of the best free LLMs is updated every month.