ween providers remains open.
8 Conclusion
Apertus 1.5 extends the Apertus 1.0 models from text to image and audio understanding through continued pretraining on 4T and 2T additional tokens, extends the context window fourfold to 262,144 tokens, and introduces the second generation of our post-training pipeline, which adds a thinking mode, more reliable instruction following, and more effective tool calling. The released models improve over their predecessors on nearly every benchmark; among fully open models of its class, the 8B leads on knowledge, instruction following, and cultural benchmarks, and both models stand strongest relative to their peers on multilingual evaluations. Thinking lifts the 8B from 51.2 to 71.4 on MATH-500; its instruction following reaches 87.1 on IFEval and 83.7 on Multi-IF, above every model we compare against; single-turn function calling on the 70B rises from 58.0 to 70.1, within 1.5 points of Llama-3.3-70B; and among fully open multimodal models the 8B leads document and chart understanding and stays ahead on long-form and lower-resource speech recognition, where dedicated audio models degrade.
The second generation of the post-training pipeline also produced most of the lessons of this release. Adding modalities through the tokenizer, with the language-model head restricted to text, upgraded the models without an architectural change. A dedicated long-context stage after supervised finetuning preserved the context extension. Filtering reasoning data by what the model already solves, and at 70B scale preceding online preference optimization with an offline stage, were decisive for the post-trained models. The 70B run, stopped before it consumed its multimodal mixture, showed that multimodal capability follows exposure.
The next generation of Apertus starts from these recipes, with the remaining gaps of Section 7.2, among them short-form hallucination and the combination of tool calling with thinking, as its first targets. As with the first release, the weights, intermediate checkpoints, data recipes, training and evaluation code, and the charter that guides the alignment are public.
Acknowledgments
This work was supported as part of the Swiss AI Initiative by a grant from the Swiss National Supercomputing Centre (CSCS). JV acknowledges support from Lia Rumantscha, RTR, and Fundaziun Patrimoni Cultural RTR. JV and MW acknowledge funding by the Swiss National Science Foundation (project InvestigaDiff; no. 10000503). We thank Ahmetcan Yavuz, Not Battesta Soliva, Manuel Tuor, Oleg Lavrovsky, Arundhati Balasubramaniam, and Marius Huber for participating in the human evaluation study for machine translation.
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