State of Open Models: Summer 2026 Observations

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State of Open Models: Summer 2026 Observations

Published<br>August 14, 2026

Update on GitHub<br>Upvote 14

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Adina Yakefu AdinaY Follow

Apolinário from multimodal AI art multimodalart Follow

Irene Solaiman irenesolaiman Follow

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.

1. The frontier is moving fast

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 anything an American lab released of its own. China's monthly ceiling ran between 754B and 2.78 trillion parameters; America's own ceiling 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.

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.

Two things made the first camp possible. Building large stopped being a differentiator. Xiaomi and Meituan both 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.

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 United States, meanwhile, is not absent from open source.

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.

When smaller models and embedding models are included, where Google, Microsoft, IBM Granite, and OpenAI’s older vision and speech models generate hundreds of millions of downloads annually, U.S. participation in open source AI is still growing.

However, the center of gravity has shifted. Google and Meta now rank well below NVIDIA in new model releases, despite being the companies that defined open model publishing in previous years. Meta’s move toward closed flagship models further highlights this change. Open source has moved from model labs to hardware and infrastructure companies .

At the frontier scale, the picture is very different. Most U.S. releases above 100B parameters this year are not new models, but built on top of Chinese models. Only a few major original American models appear at this scale: Thinking Machines’ Inkling (952B), NVIDIA’s Nemotron 3 Ultra (561B), Nemotron 3 Super (124B), and Arcee AI’s Trinity-Large (399B).

AMD contributed many conversions but no original model at this scale. This work is still important: it enables trillion-parameter Chinese models to run efficiently on American hardware. But it represents a distribution and optimization layer rather than model creation .

Meanwhile, Chinese open models are increasingly optimized for domestic chips, the same competition in reverse, where models are designed around specific hardware ecosystems.

2. Attention ≠ Adoption

We took the top 25 model repositories by downloads accumulated this year and the top 25 by likes. Exactly one repository appears in both lists.

We counted downloads inside the window rather than lifetime, so nothing is credited for merely having existed longer, and controlling for age makes the split sharper. Not one model published in 2026 reaches the download top 25, while...