OS –> Prod Survey

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

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.

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.”

Read Raffi's full letter here →Download the report here ↓

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

0%

Capability gap to the top closed models — at parity on coding, behind on reasoning

0×

Fall in GPT-4-class inference cost in 36 months: $20 → $0.40 per 1M tokens

01The current state of open-source AI

Parity reached. The contest is one layer up.

Open weights are no longer a compromise. They are where the work happens: a majority of production tokens now route through them, and the five highest-volume models on OpenRouter are all open. Closed models still lead at the frontier, on reasoning and multimodality, but the frontier is not what most workloads need. Commodity inputs do not hold pricing power. Value moves up, to the agentic harness.

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.

Inference fell 50× in 36 months

GPT-4-equivalent price per 1M tokens — faster than dotcom-era bandwidth or PC-compute price curves. Log scale.

Sources: Stanford HAI AI Index 2025 (280× GPT-3.5-class drop over 18 months); Epoch AI (9–900× annual decay); Nov 2025 MIT study (5–10×/yr at the frontier, hardware-adjusted).

Open weights win the tokens

The share of tokens routed on OpenRouter through open-weight models grew from a negligible base to a third by late 2025 to a majority by mid-2026.

Source: OpenRouter 100T-token study (Nov 2024–Nov 2025) and live leaderboard; intermediate points interpolated. By request count, closed US providers still lead — the open lead is a token-volume lead, concentrated in coding and agentic workloads.

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.

Open ships easy. Open deploys hard.

Data from the Mozilla / SlashData 2026 developer survey. Open models lead in adoption: 79% of developers adding AI functionality use them, against 71% for closed, and the two are largely complementary, with half of developers using both. But production is where teams stall: only 51% of open-model teams reach production versus 63% for closed. The gap is operational tooling and trust, not model capability.

Open models lead in adoption, and mostly coexist with closed

Share of developers adding AI functionality to their applications who currently use each model type, and how the two overlap.

Open models

79%

Closed models

71%

How they combine

29% open only Use open-source models exclusively" style="width:29%;background:var(--green)">29%OS only

50% use both The two are largely complementary" style="width:50%;background:var(--seed-1)">50%Both

21% closed only Use closed-source models exclusively" style="width:21%;background:var(--grey-1)">21%CS only

Source: Mozilla / SlashData 2026 developer survey. Open and closed aren't substitutes for most teams: 50% run both, 29% open only, 21% closed only.

Where open adoption peaks, and where closed still edges it

Open-model adoption by region. Greater China and East Asia lead at 89%; South America and Western Europe are the only two regions where closed adoption exceeds open.

Same survey, by developer region. In South America and Western Europe, and only there, closed-model adoption runs ahead of open.

Production rate by company size

If the gap were about resources, scale would close it, and it doesn't. Closed climbs 54% → 73% with scale. Open barely moves: 53% → 57%.

Closed modelsOpen models

Enterprises can buy their way through closed deployment. Open deployment waits on tooling nobody has finished. Source: Mozilla / SlashData 2026 developer survey.

Why teams churn: challenges with open models

Δ = churned − still using, in percentage points. The biggest gaps (performance, integration, maintenance) are operational, not capability. Hover the bars.

Still using openChurned away

Mozilla survey, n=1,410. “What are the main challenges you face when working with open or open-source AI models?”

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.

ChallengeW. Europe & IsraelN. AmericaGreater ChinaSouth AsiaEast Asia ex GCS. AmericaE. Europe & CISOceaniaAll

High infrastructure or compute costs25%26%29%28%28%28%29%18%27%

Security, privacy, or compliance concerns20%27%18%39%29%28%25%22%26%

Ongoing maintenance and updates27%26%18%26%20%31%21%25%24%

Complexity of deployment, hosting, or scaling27%24%19%24%11%30%26%25%23%

Lack of specialised support17%16%21%31%24%23%23%32%22%

Difficulty evaluating or comparing models14%17%14%23%16%26%25%18%18%

Difficulty fine-tuning or customising22%18%18%20%11%22%18%12%18%

Difficulty integrating into existing systems19%21%14%20%7%26%19%20%18%

Insufficient documentation or learning resources18%15%15%17%15%20%24%15%17%

Model performance is not good enough18%15%13%22%16%17%19%8%17%

No major challenges9%21%16%5%14%4%8%12%12%

Weighted sample size28627720619216414798391411

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.

02The open-source AI stack

The open stack scores high on capability, low on operations.

Nine layers and 48 components of the stack scored across 10 criteria (1–5). Click a layer to open its components: each carries its own criterion scores, maturity grade, open-vs-closed parity verdict, and surfaces some of its most-starred open-source projects.

Hover any cell for detail.

StrongViable, but fragmentedEarly stage

Strong (≥4.0) 3.5–3.9 3.0–3.4 2.5–2.9 Weak (

~$24.8B

in unrealized annual savings — the Nagle–Yue study for the Linux Foundation's estimate of the open-vs-closed price asymmetry, at ~6× the cost per call for comparable capability

Where developers route by cost, they route to open weights.

04Why it's happening everywhere

Open isn't a vendor choice. It's a sovereignty choice.

More than 70 national AI strategies are live. The strategic question has shifted from whether to have a national AI policy to which layer of the stack a country can own.

Click a marker or a country below.

The case for open is optionality

Optionality stopped being abstract in June 2026, and it stopped being a procurement question. Three days after Claude Fable 5 went on sale, a single government's export order forced Anthropic to cut access for every foreign national on earth. No other capital was consulted. None could have been. Selective compliance was impossible, so the models went dark for everyone at 5:21 p.m. on a Friday. Anyone who had built on that model inherited a shutdown they had no warning of and no part in. A provider can switch off a model. Nobody can switch off a copy already running on a machine you hold, and that holds whether the machine is a startup's server or a national supercomputer. For a company, weights on disk are a hedge. For a state, they are the difference between a policy and a permission.

The strategic case for open is the ability to leave, and the cloud era proved the cost of its absence:

The exit penalty is real Moving a single petabyte out of AWS S3 runs roughly $90,000–$120,000 in egress fees">$90–120kto move one petabyte out of AWS S3

IDC About 80% of enterprises are now repatriating some workloads; 61% report cost reductions above 25%">80%of enterprises now repatriating workloads

37signals Cut a $3.2M annual cloud bill to under $1M; AWS waived a $250,000 egress fee to let it leave">$3.2M → 2.5×what GEICO's cloud costs ran over plan

Closed model APIs reproduce the same trap: build on a proprietary endpoint and you inherit the vendor's pricing changes with no clean exit. Open weights are exit rights.

The largest source of open weights is China. By design.

Cumulative Hugging Face downloads, March 2026:

In February 2026 Qwen out-downloaded the next eight organizations combined. On OpenRouter, Chinese open-weight models rose from under 2% of tokens in late 2024 to more than 45% of weekly traffic by April 2026, and about 61% among the ten most-used models. DeepSeek reports 26,000+ enterprise accounts; 58% of new AI startups in 2025 included it in their stack, even as at least eight jurisdictions restricted the hosted service. The resolution is architectural: enterprises ban the hosted app and adopt the weights anyway, self-hosted or via Western endpoints.

This is intentional policy. The State Council's "AI Plus" Initiative (Aug 2025) and the national Five-Year Plan (Mar 2026) codify open-source proliferation as a core directive, and releasing public weights doubles as a macro hedge against semiconductor export controls, offloading global inference onto end users' local ha

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