A List That Hurts

Buried in Hugging Face's "State of Open Models: Summer 2026" report, published August 14, is a number nobody chasing shiny new releases wants to see: cross-reference the top 25 repositories by downloads accumulated in 2026 against the top 25 by likes, and exactly one repository appears on both lists. Worse, not a single model published in 2026 made the download top 25 — thirteen of them date back to 2022.

In other words, almost none of the blockbuster open-weight launches flooding your timeline are in production dependency lists. (Original report; this article is based on the official HF blog post and its GitHub mirror.)

Likes and downloads record two different acts

The report's authors put it plainly: a like says a release matters, and concentrates on frontier models in the weeks after they ship; a download says something is wired into a pipeline that runs on a schedule, accruing to small, stable models over years. Their verdict — treating either as a proxy for the other is the most common mistake in coverage of the Hub, including HF's own earlier work.

The extreme sample: the veteran sentence-embedding model all-MiniLM-L6-v2 was pulled 1.55 billion times in seven months against just 5,156 likes; Kimi-K3, the most talked-about release, saw roughly 60 downloads per like it received. The two numbers live in different economies. The underlying distribution is just as extreme: about 85.6% of model repositories have fewer than 200 lifetime downloads, while 1.5% of repositories account for 99.2% of all downloads.

Frontier-only vs. full-spectrum: a 55× gap

Splitting downloads by model size is the sharpest China-US contrast in the report. Effectively all of MiniMax's 2026 downloads came from models above 70B parameters, along with 88% of Moonshot's, 55% of DeepSeek's and 39% of Z.ai's. No large American account looks like this: Google, Microsoft and IBM Granite recorded essentially none of their 2026 downloads above 70B, with NVIDIA and Meta at just 14% and 9%.

The payoff gap between the two routes is enormous: Moonshot's frontier-only portfolio recorded about 37 million downloads over the year, while Qwen's full-spectrum strategy — spanning the 2.4T-parameter Qwen 3.8 Max down to 27B variants — reached about 2,045 million. A factor of roughly 55. The report argues a size profile is now a statement of intent rather than capability: a frontier-only portfolio bets everything on benchmark position and API demand; a full-spectrum portfolio is a bid to be the family developers standardize on. Frontier-only remains viable at all because llama.cpp pushed the local-inference ceiling upward — GGUF builds of DeepSeek-V4-Flash at roughly 284B and Kimi-K3 at roughly 2.8 trillion parameters mean a trillion-parameter mixture-of-experts spread across a few consumer machines, something that simply did not exist a year ago.

The most permissive licensing era may be peaking

Of 178 Chinese releases above 20B parameters this year, 59% carry Apache 2.0 and 22% carry MIT, almost none with non-commercial restrictions; DeepSeek and Z.ai ship models between 700 billion and 1.65 trillion parameters under plain MIT. The American side of the same size band: only 29% Apache or MIT, 41% under custom terms, and 30% declaring nothing at all. But in the last few weeks the wind shifted — Kimi K3 and Qwen 3.8 2.4T, the two largest models, started adding non-commercial restrictions and revenue share requirements to their licenses. The report is blunt: whatever these releases are for, it is not license revenue; the return has to come from API and cloud business, hardware and platform positioning, or the ecosystem position itself.

Agents are the new user — with no incumbent

The agent-usage dataset published in July made "who is calling the Hub" visible for the first time. Claude Code led July with 44.4% of agent traffic, but a single month conceals the real finding — it held 67.8% in April and 64% in May, while Codex climbed steadily from 10.4% to 20.8%. Nearly a quarter of July's agent traffic came from harnesses not yet named in the dataset; in May that figure was 59.8%. This is a market with no incumbent, where one release or one changed default can move half the traffic in a month. HF closes the report stating it directly: agents became the number-one user of the HF Hub for the first time.

So what

For developers, the report's value is a correction to selection methodology: the new models flooding your feed measure attention; what you actually depend on is read from download curves and derivative ecosystems — chasing novelty and provisioning production pipelines are two different procurement paths. For the industry, watch the licensing section: when the two largest open models start talking about revenue share, the shelf life of "most permissive" may only be a few quarters.

Hype deceives. Dependencies don't.