GPAI Training Transparency

Quality Assessment AI Act Article 53(1)(d) Public Summaries

The AI Act's Article 53(1)(d) requires General-Purpose AI (GPAI) model providers to publish "a sufficiently detailed summary about the content used for training ... according to a template provided by the AI Office". Enforcement by the AI Office has begun as of 2nd August 2026.

To support this, we discovered 39 public summaries, which we evaluate across two aspects: Transparency and Usefulness and assign a score using our developed methodology. Our findings expose challenges regarding accessibility, vagueness and incompleteness of information, and limitations this poses on rightsholders. We also highlight missing summaries for 59 models based on our preliminary analysis. Please get in touch if you find any summaries or would like to know more about our work.

See our peer-reviewed article: Dick A. H. Blankvoort, Harshvardhan J. Pandit, and Maximilian Gahntz (2026). Quality Assessment of Public Summary of Training Content for GPAI models required by AI Act Article 53(1)(d). 9th ACM Conference on Fairness, Accountability, and Transparency (FAccT), Montreal, Canada. Zenodo. DOI:10.1145/3805689.3806755

Media Coverage:
Euractiv "Researchers have trouble finding AI training data summaries (2 Mar 2026);
Tech Policy Press "How Big AI Developers are Skirting a Mandate for Training Data Transparency (4 Mar 2026).

The table below shows an overview evaluated public summaries with A+ as the highest grade score and F the lowest. A separate list is provided further below with published summaries currently being evaluated, and another list for models which require a public summary is missing despite being necessary. You can click the model name to go to the detailed evaluation page which has more information, a link to the summary, and our evaluation notes. You can also see a detailed overview of scores for each section of the public summary. You can also click on the columns to sort the table by name or by score.

Evaluated Public Summaries

Model Provider Transparency Usefulness
Apertus Swiss AI Initiative Swiss AI Initiative A   A+
FIBO Bria AI Bria AI B+ A+
Bria 3.2 Bria AI Bria AI B+ A  
SmolLM3-3B HuggingFace HuggingFace B+ B+
Domyn Large Domyn Domyn B+ B+
Bielik v3 11B Instruct SpeakLeash SpeakLeash B+ C+
Adobe Firefly Adobe Adobe C+ B+
Inkling Thinking Machines Thinking Machines C+ C+
Inkling Small Thinking Machines Thinking Machines C+ C+
FLUX.3 Black Forest Labs Black Forest Labs B   C  
Nova 2 Lite Amazon Amazon C+ C+
FastwebMIIA Fastweb Fastweb C   C+
MAI Cyber 1 Flash Microsoft AI Microsoft AI C+ C  
Minimax M3 Minimax Minimax C+ D+
MAI Code 1 Flash Microsoft AI Microsoft AI C+ C  
MAI-Image-2.5 Microsoft Microsoft C+ D+
Ministral 3 14B Mistral AI Mistral AI C   C  
Ministral 3 3B Mistral AI Mistral AI C   C  
Ministral 3 8B Mistral AI Mistral AI C   C  
MAI-Image-2 Microsoft Microsoft C+ D+
GPT-5.6 Luna OpenAI OpenAI C+ D+
Apertus v1.5 Swiss AI Initiative Swiss AI Initiative C   C+
GPT-5.5 OpenAI OpenAI C+ D+
Gemma 4 Google Google C   C  
Gemini 3 Pro Google Google C   C  
Mistral Large 3 675B Base 2512 Mistral AI Mistral AI C   C  
Mistral Small 4 119B 2603 Mistral AI Mistral AI C   C  
Grok 4.5 xAI xAI C   C  
C4AI Command A Plus 05 2026 Cohere Cohere C   C  
Claude Opus 4.7 Anthropic Anthropic C   D+
Claude Mythos Preview Anthropic Anthropic C   D+
Claude Opus 5 Anthropic Anthropic C   D+
Claude Sonnet 5 Anthropic Anthropic C   D+
Claude Mythos 5 / Claude Fable 5 Anthropic Anthropic C   D+
Claude Opus 4.8 Anthropic Anthropic C   D+
Muse Image Meta Meta C   D+
Muse Spark Meta Meta C   D+
Phi-4 Microsoft Microsoft D   F  

Summaries currently being evaluated

We are currently evaluating 1 public summaries. Their scores and our notes will be published shortly. In the meantime, you can access the public summary through the details page by clicking the model name.

Model Provider
PLLuM 2512 Base Ministry of Digital Affairs of Poland Ministry of Digital Affairs of Poland

Models with missing summaries

Based on our preliminary analysis, we have assessed the following models as requiring a mandatory public summary, but which we could not discover.

Model Provider
Claude Sonnet 4.6 Anthropic Anthropic
FLUX.2 [max] Black Forest Labs Black Forest Labs
Apriel 1.5 15B Thinker ServiceNow ServiceNow
Claude Opus 4.6 Anthropic Anthropic
GPT-5.6 Sol OpenAI OpenAI
FLUX.2 Klein Black Forest Labs Black Forest Labs
GPT-OSS OpenAI OpenAI
Granite 4.1 30B Base IBM IBM
FLUX.2 Black Forest Labs Black Forest Labs
GPT-5.6 Terra OpenAI OpenAI
Claude Opus 4.1 Anthropic Anthropic
GPT Image 2 OpenAI OpenAI
Sora 2 OpenAI OpenAI
Granite 4.0 H Small Base IBM IBM
Palmyra X5 WRITER WRITER
Claude Haiku 4.5 Anthropic Anthropic
Claude Opus 4.5 Anthropic Anthropic
North Mini Code 1.0 Cohere Cohere
Claude Sonnet 4.5 Anthropic Anthropic
Veo 3.1 Google Google

Why we started this project:

  • We contend that compliance cannot be fait accompli, and that the public summaries are a key factor in creating transparency and enabling rights enforcement. Towards this, our work also acts as a guide for providers who are yet to publish their summaries to consider how to do so with the highest possible quality and utility.
  • Compliance also invites practices that are intentionally or unintentionally deficient in achieving the goals. Our work serves as a useful tool for describing how and where and why certain practices are 'bad', e.g., where they use obfuscation, do not provide stated information. Using this, we can detect trends or patterns in whether the same issues occur in many summaries, and if so, how they can be collectively addressed through guidance, or enforced with priority.
  • The largest challenge in undertaking this work has been finding public summaries as there is no consistent format or practice for how they should be provided. For this, we provide recommendations.
  • The template for public summaries provided by the AI Office is intended to be revised with time to improve the state of documentation as well as to better guide the providers. We also provide recommendations for these to improve the quality and accessibility of the public summaries.