nsumption
6.4 Engineering Challenges and Solutions
7 Responsible Release and Governance
7.1 License and Acceptable Use
7.2 Limitations
7.3 Broader Impact
7.4 Community and External Evaluation
8 Conclusion
References
A Contributions
B Vision Datasets by Training Stage
C Audio Datasets by Training Stage
D Image Dataset Exploration Detailed Breakdown
E Tokenizer Reconstruction by Category
F Long-Context Data Mixture Tables
G Training Configurations
H Supervised Finetuning Details
H.1 Per-Benchmark SFT Ablation Results
H.2 SFT Data Decontamination Details
I Preference Alignment Data
I.1 Judge Rubric Prompts
I.2 Prompt Set Composition
I.3 Response Model Pool
I.4 Completion Generation
I.5 Score Distribution
J Alignment Ablations
J.1 DPO Ablations
J.1.1 Apertus 1.5 8B
J.1.2 Apertus 1.5 70B
J.2 Judge Ablations
J.2.1 Judging Aspects Simplification
J.2.2 Choice of Judge Model
K Chatbot System Prompt
L Evaluation Details
L.1 Full Post-Training Lineage
M Pre-Release Manual Evaluations
M.1 General Information About Pre-Release Evaluation
M.2 Chemical, Biological, Radiological, Nuclear, and Explosive Indiscriminate Weapons (CBRNE)
M.3 Dual Use and Insurgency
M.4 LLM-Assisted Information Operations
M.5 (Child) Sexual Abuse
M.6 Offensive Cybersecurity
M.7 Multimodal Prompt Injection
M.8 Harmful Disinformation
M.9 Harmful Health Advice
M.10 Private Person Defamation
N Coverage of Common AI Security Framework Risks
1 Introduction
The most capable large language models (LLMs) are accessible only through proprietary interfaces, and even the strongest openly released models typically publish their weights without the data, code, recipes, and values behind them. Fully open models are increasingly rare as the complexity and cost of LLM development have grown significantly, and they still trail their open-weight peers in capabilities. Apertus exists to close this gap. We understand full openness to extend across five axes: open weights, open data, open code, open recipes, and open values. Every claim in this report can therefore be traced to released artifacts, and the principles that shape the behavior of our models are published documents rather than internal policy.
Apertus is a collaborative effort led by ETH Zürich, EPFL, and CSCS, with contributors from many further institutions, to develop AI in an inclusive and trustworthy manner, and to provide a foundation on which developers build products and services. Pluralism is a founding goal: from the first release onward, Apertus pursues broad coverage of languages and cultures, and this focus continues in the present release. A second commitment is responsible development, guided by Swiss and European ethical and regulatory standards, while striving for performance and scale. LLMs are a central technology of our time, and we believe research institutions should engage with them directly. Apertus provides a scientific basis for research and development on open models by making its model weights, code, data, training recipe, and values available for study and reuse. This enables researchers and practitioners to examine the training process, deploy models independently, retain control over associated data flows, and develop the expertise needed to teach the next generation of engineers and researchers.
We released the first generation of models, Apertus 1.0, with 8B and 70B parameters in September 2025, trained from scratch on 15T tokens of openly documented text spanning over 1000 languages on the Alps supercomputer at CSCS (Hernández-Cano et al., 2026). Its data pipeline respected machine-readable opt-outs of domain owners, applying them retroactively to earlier crawls, removed personally identifiable information, and drew only from permissively licensed sources, and training suppressed verbatim memorization. The Apertus charter, published alongside the models, states the principles that guide their behavior, and the release included transparency documentation following the EU AI Act template, inte