Apertus 1.5 Release Notes

Key Enhancements Training Process Multimodality Through the Tokenizer Pre-Training Post-Training Evaluation Results Evaluation Cards: Apertus ❤️ EvalEval Apertus 1.5 Reproducibility Model Availability How to Get Involved Citation Our latest release, Apertus 1.5, extends the 8B and 70B Apertus 1.0 models with image/audio understanding, an optional thinking mode, a four times longer context window, improved instruction-following, and better tool use. The updated 8B and 70B model weights were released to Hugging Face on July 24, 2026 under an Apache 2.0 license.

Further details are now available from the project Technical Report, and summarized here. Apertus 1.5 is a continued pretraining of the first generation model: adding 4 trillion tokens of text and multimodal training data to the 8B model and 2 trillion tokens to the 70B model, for a total of 19T and 17T, respectively. As with every Apertus release, the models are fully open: open weights, open data, open values, and full training details. We introduce multimodality in v1.5 models in an early-fusion manner, where images and audio enter Apertus as discrete tokens in the same sequence with text tokens, instead of using a separate vision encoder. Images are tokenized with the Emu3.5 tokenizer (a 512×512 image becomes 1,024 tokens) and audio with WavTokenizer (40 tokens per second). We only extend the vocabulary, so the architecture stays unchanged and training runs at text-only speed. The models understand images and audio but generate only text. Starting from Apertus 1.0, we continue pretraining in four stages: Overall, more than half of the training tokens are text-only, which preserves the language abilities of Apertus 1.0. The text data is refreshed with sources such as FineWeb2-HQ+, FinePDFs, and Nemotron-CC. All new data, multimodal included, meets the same bar as before: publicly available, permissively licensed, and with robots.txt opt-outs respected retroactively. Every source is listed in apertus-data-transparency. Post-training runs in five stages: SFT → long-context SFT → RL → DPO → charter alignment (SDPO). Alongside the extended model capabilities, we also significantly expanded the scope of evaluations. Beyond the existing text-based evaluations used for Apertus 1.0, we added multimodal evaluations (image & audio), and thinking-mode evaluations. The multimodal evaluations are done through MLLM-eval-suite pipeline, merging two major multimodal evaluation frameworks lmms-eval and VLMEvalKit.