The State of Open Source AI — V1.0 · July 2026

Build with us.

A letter from our CTO, Raffi Krikorian

In New Zealand’s far north, a Māori broadcaster trains speech models for te reo — a language too small for any market — under a license that keeps the data with its people. PwC, one of the largest accounting firms in the world, fine-tuned an open model on the language of finance and runs it today for hundreds of clients, on its own hardware, with no per-token meter running. Researchers in Lausanne built an open medical model with the Red Cross, tuned to its humanitarian guidelines, and are preparing clinical trials at home and in Tanzania. In East Africa, farmers diagnose cassava disease with a model that runs on the phone itself, offline, in fields the cloud has never reached. In Switzerland, a public consortium trained a national model on public supercomputers and released all of it: weights, data, training code. None of them asked permission, and none of them could have rented this. They own it — that is the whole idea.

Open-source and open-weight AI now anchor one of the fastest-growing builder ecosystems in the history of software. Hugging Face alone hosts 2.5 million public models and 13 million users. A third of the Fortune 500 are among them. On OpenRouter, where developers route real production traffic, open-weight models went from a sliver of usage to roughly a third by late 2025. Just six months later, the platform moves 25 trillion tokens a week — five times as much — and the largest single source of that traffic is an open model. Developers are responding to what the models can do and what they cost. And on both counts open has become the practical choice.

This spring, the strongest closed model scored 60 and the strongest open model 54. A year earlier, the leading open model managed 22. Closed systems still lead on the hardest problems. But for what most builders actually ship — where price, control, and deployability matter — open models have crossed from promising to ready. Anyone still waiting for open source AI to grow up can stop waiting. It already has.

Governments are moving, too. The European Commission has proposed an “open source first” rule for how public institutions buy AI, and Canada has set a national target to lift business adoption from 12 percent to 60. When communities, markets, and governments converge on the same thing at once, they are telling you where this is heading: toward more intelligence, in more hands, and owned by more people.

None of this is inevitable, and the other future on offer is seductive. Picture a handful of validation machines reading the world back to you, smooth and confident and sourced to nothing you can check. The bazaar of a billion arguing voices is muffled by a polished concierge that answers to its owner. We got a preview this June, on a Friday afternoon, when one of the most advanced models went dark everywhere because a government sent a letter. Every business renting that model discovered an off switch that belonged to someone else.

We have been here before. Mozilla exists because one company tried to own the front door to the web, and an open community rose up to make sure it never could. Twenty-five years later, someone is running the same play. We bet on open the first time. Open won. Together, we can do it again.

Our belief is simple: the path forward is competition and interoperability. We believe in a world of many models, standard ways to plug them together, and the freedom to walk away from any vendor at any time. Open has a record here. It grew the pie and let more people own a slice of it.

Read what follows as a map: where open AI is winning — some numbers surprised even us — and where it is exposed. A case that hides its weak points is an advertisement.

The builders are already building. A rented future has deeper pockets; an owned one has more hands — millions more — and this story ends the same way every time it is told: the many, building in the open, outbuild the few behind walls.

Build with us.