State of Open Models: Summer 2026 Observations

In the AI world, time feels compressed. A few months after our spring report in our biannual analysis worked through the ecosystem, there are quite a few findings that we have observed until this summer. This report lays out these observations from January to August 2026 and presents the data behind each one.

Models and datasets on HF hub are growing on a daily basis. Public model repositories grew from 2.43 to 2.96 million over the period, datasets from 711,000 to 1 million, Spaces from 1.00 to 1.44 million. The distribution underneath stays extreme, roughly 85.6% of models have fewer than 200 lifetime downloads, and 1.5% of repositories account for 99.2% of all downloads. Everything below happens inside that shape.

There used to be a clear progression path: labs would start by releasing smaller models and gradually work their way toward the top end of the scale. In 2026, several Chinese labs skipped this progression entirely.

In almost every month of 2026, the largest and most performant open model from a Chinese lab was larger than any model an American lab released. China's monthly ceiling ran between 754B and 2.78 trillion parameters; U.S. models stayed under 130B in five of seven months, the exception being NVIDIA's Nemotron 3 Ultra at 561B in May and June, and Inkling from Thinking Machines Lab.

Two things made the first camp possible. Building large stopped being a differentiator. Xiaomi, Ant Group and Meituan all cleared a trillion parameters this year, and neither was a household name in open weights twelve months ago. And a lab no longer has to ship a small model to be reachable, because the community's quantization layer will make a large one runnable within days, a dependency we return to below.

At the frontier scale, some U.S. model releases above 100B parameters this year are built on top of Chinese models or leverage artifacts from Chinese labs, such as Thinking Machines’ Inkling (952B). Major original American models include NVIDIA’s Nemotron 3 Ultra (561B), Nemotron 3 Super (124B), and Arcee AI’s Trinity-Large (399B).

AMD contributed many conversions. This work is important: it enables trillion-parameter models to run efficiently on U.S. hardware. This represents a distribution and optimization layer .

Qwen derivatives have increased at roughly 180–210 new repositories per day throughout the first seven months of 2026, showing that adoption is not driven only by individual launches. Qwen has become part of the default workflow for developers deciding what models to fine-tune and deploy.

Several factors contributed to this position. First, consistency. Qwen has maintained a regular release cadence, continuously updating its model family rather than relying on occasional flagship releases. Second, coverage. It publishes models across a wide range of sizes and use cases, allowing developers to stay within the same ecosystem whether they need a small local model or a larger deployment model. Third, openness. Apache 2.0 licensing reduces friction for modification, redistribution, and commercial use.

The chart splits the labs into two camps. Moonshot , MiniMax , Xiaomi and Z.ai publish almost nothing below 70B, so a developer's first encounter with them is a model too large to run on anything they own. Tencent and Alibaba Qwen cover the whole range instead, from under 1B upward.

That leaves the size profile as a statement of intent rather than of capability. A frontier only portfolio stakes everything on benchmark position and API demand. A full spectrum portfolio is a bid to be the family developers standardise on. Both are rational, they are playing for different prizes.

The two organizations publishing the most new open models this year are also the companies making the hardware: AMD and NVIDIA . Each released more than 200 new model repositories, far ahead of the rest of the field, with LiquidAI ranking third at around 100. Hardware vendors have realized that open models are a way to sell chips: a model optimized for your hardware and freely available is the clearest proof that the hardware works.