The ATOM Report: Measuring the Open Language Model Ecosystem
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917532777054208 |
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| author | Lambert, Nathan Brand, Florian |
| author_facet | Lambert, Nathan Brand, Florian |
| contents | We present a comprehensive adoption snapshot of the leading open language models and who is building them, focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama, that are the foundation of an ecosystem crucial to researchers, entrepreneurs, and policy advisors. We document a clear trend where Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts. We study a mix of Hugging Face downloads and model derivatives, inference market share, performance metrics and more to make a comprehensive picture of the ecosystem. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07190 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | The ATOM Report: Measuring the Open Language Model Ecosystem Lambert, Nathan Brand, Florian Computers and Society Artificial Intelligence Machine Learning We present a comprehensive adoption snapshot of the leading open language models and who is building them, focusing on the ~1.5K mainline open models from the likes of Alibaba's Qwen, DeepSeek, Meta's Llama, that are the foundation of an ecosystem crucial to researchers, entrepreneurs, and policy advisors. We document a clear trend where Chinese models overtook their counterparts built in the U.S. in the summer of 2025 and subsequently widened the gap over their western counterparts. We study a mix of Hugging Face downloads and model derivatives, inference market share, performance metrics and more to make a comprehensive picture of the ecosystem. |
| title | The ATOM Report: Measuring the Open Language Model Ecosystem |
| topic | Computers and Society Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2604.07190 |