Nathan Lambert@natolambert

2026-04-08 22:43·85天前

AI 摘要

本报告基于Interconnects与ATOM Project数据，手动筛选约1.5K个重要语言模型，通过下载量、衍生模型数量及OpenRouter推理份额等多维度指标，分析开源模型采用趋势。数据显示，以Qwen、Kimi为代表的中国模型全球采用率持续加速领先，其中Qwen 3.5、Nemontron 3、Kimi K2.5等近期模型在相对采用指标(RAM)中表现突出。研究同时指出，大型模型仍是Qwen相对竞争力较弱的领域。该工作旨在为开源生态系统提供更准确的公开数据与趋势洞察。

New report with @xeophon is out with the latest open model adoption data we have gathered for Interconnects & The ATOM Project. At the surface level， we can see Chinese models continuing to accelerate in adoption.

The report details much more.

We manually curate ~1.5K of the most important language models， creating a specific set of models to focus our analysis on （excludes embedding models， local inference models like MLX/GGUF， etc to have accurate download rankings）.

Studying other adoption metrics， such as derivative models and inference share on OpenRouter， to show how they correlate with downloads， while often sifted in time. China has a strong lead here too.

Better classification of downloads across model sizes. Large models still are the models where Qwen is least competitive， relative to other model builders.

Expansion of our Relative Adoption Metric （RAM） to show standout recent models （we'll check Gemma 4 on Friday）； Qwen 3.5， Nemontron 3， Kimi K2.5， all showing very strong adoption.

Overall， this is another step towards formalizing and making public better data on the open language model ecosystem， so the community can better understand the impact and trends of its adoption. More on this soon！

开源生态现象/趋势

Nathan Lambert@natolambert · X

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2026-04-08 22:43·85天前

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AI 摘要

The report details much more.

We manually curate ~1.5K of the most important language models， creating a specific set of models to focus our analysis on （excludes embedding models， local inference models like MLX/GGUF， etc to have accurate download rankings）.

Studying other adoption metrics， such as derivative models and inference share on OpenRouter， to show how they correlate with downloads， while often sifted in time. China has a strong lead here too.