The Foundation Model Transparency Index

Changes from 2024

The 2025 edition is the first year where the indicators change. Drawing on insights from the past two FMTI editions and changes in the practice of foundation model development, we make significant changes to the transparency indicators. This includes: raising the bar to ensure that the disclosed information is useful, adding indicators to reflect new salient topics, and targeting organizational practices to focus on the AI companies rather than just the technologies they build.

Like in 2024, we ask companies to provide us with a report and proactively disclose information about the transparency indicators. While the majority of companies did so, some key companies did not. Therefore, we manually gather information about 6 companies (Alibaba, Anthropic, DeepSeek, Midjourney, Mistral, xAI) as the basis for our scoring. For these 6 companies, we additionally augment the manual collection of information with an AI agent to identify relevant content that the FMTI team may have overlooked.

• Companies engaged less with the Index. 9 companies were scored in both 2024 and 2025. While all 9 of these companies prepared transparency reports to engage with the FMTI in 2024, only 7 did so in 2025: for Mistral and Anthropic, the initial reports were instead prepared by the FMTI team. Overall, 30% of the companies we contacted agreed to submit transparency reports in 2025, dropping from 74% in 2024.
• Not all companies became less transparent. While most companies decreased their scores, two companies significantly increased their scores (Writer and IBM).
• Reductions in transparency are not homogeneous. Within the 7 companies that scored lower in 2025, OpenAI decreased by 14 points, Meta decreased by 29 points, Mistral decreased by 37 points, and the remaining companies dropped by less than 10 points.

Evolution of scores across three years

Rankings flip from 2023 to 2025. Six companies were scored across all three years. Of these six, Meta and OpenAI drop from 1st and 2nd position in 2023 to the 5th and 6th in 2025. On the other hand, AI21 and Anthropic rose from the 4th and 5th position in 2023 to the 1st and 2nd in 2025.

Scores across major dimensions of transparency

Training data continues to be opaque. In 2023 and 2024, companies tended to score poorly in the Data subdomain, being opaque about topics like copyright, licenses, and PII. This continues to be the case in 2025: companies tend to score poorly in the Data Acqusition and Data Properties subdomains.

Many companies do not disclose basic information about the model itself. Amazon, Google, Midjourney, Mistral, OpenAI and xAI do not score any indicators in the model information subdomain, such as the basic model information indicator (which includes input modality, output modality, model size, model components, and model architecture).

Scores by release strategy

Openness correlates with—but isn't sufficient for—transparency. The 5 open-weight developers outscore the 8 closed-weight developers overall and on every domain. However, high-profile open model developers are still quite opaque: Alibaba, DeepSeek, and Meta all score in the bottom half of companies.

Scores by geographic region

US companies score higher on-average (but also hold the two lowest scores). The discrepancies between the 9 US companies and the 4 non-US companies are driven by the downstream domain, specifically highlighting geographic differences in disclosures about usage data, post-deployment impact measurement, and accountability mechanisms.

Scores by business model

B2B companies are considerably more transparent. The gap is especially apparent in Upstream where the 4 B2B companies on average receive around five times as many points as the 7 hybrid and 2 consumer developers.

Board

The FMTI advisory board works directly with the Index team, advising the design, execution, and presentation of subsequent iterations of the Index. Concretely, the Index team will meet regularly with the board to discuss key decision points: How is transparency best measured, how should companies disclose the relevant information publicly, how should scores be computed/presented, and how should findings be communicated to companies, policymakers, and the public? The Index aims to measure transparency to bring about greater transparency in the foundation model ecosystem: the board's collective wisdom will guide the Index team in achieving these goals. (Home)

Board members

Arvind Narayanan is a professor of computer science at Princeton University and the director of the Center for Information Technology Policy. He co-authored a textbook on fairness and machine learning and is currently co-authoring a book on AI snake oil. He led the Princeton Web Transparency and Accountability Project to uncover how companies collect and use our personal information. His work was among the first to show how machine learning reflects cultural stereotypes, and his doctoral research showed the fundamental limits of de-identification. Narayanan is a recipient of the Presidential Early Career Award for Scientists and Engineers (PECASE).

Daniel E. Ho is the William Benjamin Scott and Luna M. Scott Professor of Law, professor of political science, professor of computer science (by courtesy), senior fellow at the Stanford Institute for Human-Centered Artificial Intelligence (HAI), senior fellow at the Stanford Institute for Economic Policy Research, and director of the Regulation, Evaluation, and Governance Lab (RegLab). Ho serves on the National Artificial Intelligence Advisory Committee (NAIAC), advising the White House on AI policy, as senior advisor on Responsible AI at the U.S. Department of Labor and as special advisor to the ABA Task Force on Law and Artificial Intelligence. His scholarship focuses on administrative law, regulatory policy, and antidiscrimination law. With the RegLab, his work has developed high-impact demonstration projects of data science and machine learning in public policy.

Danielle Allen is James Bryant Conant University Professor at Harvard University. She is a professor of political philosophy, ethics, and public policy and director of the Democratic Knowledge Project and of the Allen Lab for Democracy Renovation. She is also a seasoned nonprofit leader, democracy advocate, national voice on AI and tech ethics, distinguished author, and mom. A past chair of the Mellon Foundation and Pulitzer Prize Board, and former Dean of Humanities at the University of Chicago, she is a member of the American Academy of Arts and Sciences and American Philosophical Society. Her many books include the widely acclaimed Talking to Strangers: Anxieties of Citizenship Since Brown v Board of Education; Our Declaration: A Reading of the Declaration of Independence in Defense of Equality; Cuz: The Life and Times of Michael A.; Democracy in the Time of Coronavirus; and Justice by Means of Democracy. She writes a column on constitutional democracy for the Washington Post. She is also a co-chair for the Our Common Purpose Commission and founder and president for Partners In Democracy, where she advocates for democracy reform to create greater voice and access in our democracy, and to drive progress toward a new social contract that serves and includes us all.

Daron Acemoglu is an Institute Professor of Economics in the Department of Economics at the Massachusetts Institute of Technology and also affiliated with the National Bureau of Economic Research, and the Center for Economic Policy Research. His research covers a wide range of areas within economics, including political economy, economic development and growth, human capital theory, growth theory, innovation, search theory, network economics and learning. He is an elected fellow of the National Academy of Sciences, the British Academy, the American Philosophical Society, the Turkish Academy of Sciences, the American Academy of Arts and Sciences, the Econometric Society, the European Economic Association, and the Society of Labor Economists.

Rumman Chowdhury is the CEO and co-founder of Humane Intelligence, a tech nonprofit that creates methods of public evaluations of AI models, as well as a Responsible AI affiliate at Harvard's Berkman Klein Center for Internet and Society. She is also a research affiliate at the Minderoo Center for Democracy and Technology at Cambridge University and a visiting researcher at the NYU Tandon School of Engineering. Previously, Dr. Chowdhury was the director of the META (ML Ethics, Transparency, and Accountability) team at Twitter, leading a team of applied researchers and engineers to identify and mitigate algorithmic harms on the platform. She was named one of BBC's 100 Women, recognized as one of the Bay Area's top 40 under 40, and a member of the British Royal Society of the Arts (RSA). She has also been named by Forbes as one of Five Who are Shaping AI.

About us

The Foundation Model Transparency Index was created by a group of AI researchers from Stanford University's Center for Research on Foundation Models (CRFM) and Institute on Human-Centered Artificial Intelligence (HAI), MIT Media Lab, and Princeton University's Center for Information Technology Policy. The shared interest that brought the group together is improving the transparency of foundation models. See author websites below.