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

Open weights closed the capability gap while the price of intelligence collapsed.

The capability gap: 8.04% → 0.5% → 3.3%

Open-vs-closed gap on Chatbot Arena over 24 months. By August 2024, the gap had collapsed to 0.5%, and in February 2025 DeepSeek-R1 briefly matched the top US model. By March 2026 it had reopened to 3.3% as closed reasoning models pulled ahead. But 3.3% is an average over a jagged frontier: open is at or near parity on coding, instruction-following and general knowledge, while the gap concentrates in reasoning, long-context retrieval and agentic tasks. The question is no longer whether open models are good enough. It's what you need for your workload. Hover the points.

Source: Chatbot Arena, Jan 2024 – Mar 2026.

OpenRouter live leaderboard — trailing month, tokens routed

The five highest-volume models are all open weights. Anthropic's closed Claude models are the next US-built entrants.

Open weightsClosed

By mid-2026 the top nine models route roughly 18T weekly tokens for Chinese-built models against ~5.5T for US-built ones — more than 3:1 (FT analysis). Where developers route by cost, they route to open weights.

The same challenges, everywhere: what blocks open by region

Share of current and churned open-model developers naming each challenge, by region. Warmer cells mean more developers blocked. The top rows are operational in every region: infrastructure cost, security and compliance, maintenance, deployment complexity. South Asia leans hardest on security and support; only North America and Greater China have more than 15% reporting no major challenges.

Source: Mozilla / SlashData 2026 developer survey (MZCS1). n=1,410 current or churned open-model developers; the Oceania column (n=39) and Eastern Europe & CIS (n=98) fall below reliable thresholds.

The venture-funded open-source ecosystem: total disclosed funding, USD M

Bars grow as you scroll. Color by region of the company.

North AmericaChinaEurope & rest of world

Selected companies; Zhipu AI and MiniMax went public (HK IPO 2026) with undisclosed totals. Corporate strategics (Nvidia, Salesforce, AMD, Google, IBM, ASML, Tencent, CATL, Schwarz Group) back the same ecosystem across model, inference, and tooling layers.

Financial maturity of the open ecosystem

Funding, valuation and revenue traction for the companies carrying the open stack. The ecosystem has moved from grants to venture scale to public markets.

Five revenue models are proven at scale: hosted inference, enterprise platforms, on-prem licensing, fine-tuning services, and harness tooling. “—” = not publicly disclosed.

The user · other agents · the worldhumans · systems · data · money

Meta-harness · Omnigent · OPA · Agent governance toolkit

The model — the weightsopen or closed · swappable · commoditizing toward zero

The model is eating the harness, and that's the opening for open

Terminal-Bench 2.0 (May 2026) made the harness look like a free lunch anyone could capture: a third-party scaffold ran Anthropic's own weights to 79.8% while Claude Code managed 58.0% on the same model, a 21.8-point spread that put the harness ahead of the weights. Eight weeks later, Terminal-Bench 2.1 reversed it. The frontier labs read that result and pulled the harness in-house: on every model where both appear, the lab's own harness now wins, and the 21.8-point gap has compressed to roughly 3 at the top: the model eating its way up the stack, weights and scaffold shipped as one product.

That integration is a moat in formation, and the labs have every incentive to deepen it. A harness tuned tightly to one lab's weights becomes a fit rather than a neutral layer. It degrades on anyone else's model, so the tighter the tuning, the less swappable the weights underneath. Lock-in arrives as a side effect of optimization. The open models have no first-party harness to answer with, which is why none appear in the verified top tier of the official board at all.

But absence isn't inability: put every model on one neutral scaffold and the gap collapses

On a fair harness the capability gap is a few points; the price gap is 5×. The strongest open model, GLM 5.2, lands a fraction behind Claude Opus 4.7 and about four points behind Opus 4.8 at roughly a fifth the cost. Integration buys the labs a second edge open deployments lack: a data flywheel where real usage through the lab's harness feeds straight back into the next model. That edge is real, and it cuts both ways: the usage exhaust trains whoever owns the harness, and the only question is whether that's the closed endpoint or your own stack. The labs have proven the harness is worth owning. The same move is open, a harness co-designed with open weights, and the window to keep it a layer is now, before the closed stacks finish welding model and scaffold into one rented product.

The write surface: the unsolved hole at the center of the harness

The opening letter

Build with us.

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.