The 2025 Foundation Model Transparency Index
DOI:
https://doi.org/10.70777/si.v2i4.17165Keywords:
AI Transparency, Foundation Models, Model Evaluation, Foundation models Downstream Impact, AI Data Acquisition, transparency indicators, transparency metrics, EU AI Act, General Purpose-AI Code of conduct, Frontier Model ForumAbstract
Foundation model developers are among the world’s most important companies. As these companies become increasingly consequential, how do their transparency practices evolve? The 2025 Foundation Model Transparency Index is the third edition of an annual effort to characterize and quantify the transparency of foundation model developers. The 2025 FMTI introduces new indicators related to data acquisition, usage data, and monitoring and evaluates companies like Alibaba, DeepSeek, and xAI for the first time. The 2024 FMTI reported that transparency was improving, but the 2025 FMTI finds this progress has deteriorated: the average score out of 100 fell from 58 in 2024 to 40 in 2025. Companies are most opaque about their training data and training compute as well as the post-deployment usage and impact of their flagship models. While companies tend to disclose evaluations of model capabilities and risks, limited methodological transparency, third-party involvement, reproducibility, and reporting of train-test overlap pose challenges. In spite of this general trend, IBM stands out as a positive outlier, scoring 95, in contrast to the lowest scorers, xAI and Midjourney, at just 14. Several groups of companies score higher than the mean: open model developers, enterprise-focused B2B companies, companies that prepare their own transparency reports, and signatories to the EU AI Act General Purpose-AI Code of Practice. The five members of the Frontier Model Forum we score end up in the middle of the Index: we posit that major companies aim to avoid particularly low rankings but also lack incentives to be highly transparent. As policymakers around the world increasingly mandate certain types of transparency, this work reveals the current state of transparency for foundation model developers, how it may change given newly enacted policy, and where more aggressive policy interventions are necessary to address critical information deficits.
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Copyright (c) 2026 Alexander Wan, Kevin Klyman, Sayash Kapoor, Nestor Maslej, Shayne Longpre, Betty Xiong, Percy Liang, Rishi Bommasani
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