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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. We also added a series of text-based benchmarks, as well as extended benchmarking for translation and multilingual capabilities, safety, and tool use. These can be found in our post-training evaluation repository, based on lm-evaluation-harness fork. While still trailing frontier open-weight models, the new release has also significantly improved performance in coding and instruction following capabilities with Apertus 1.5 8B outperforming similarly sized open-weight models on IF and multilingual tasks. Thinking further improves performance on challenging reasoning-heavy benchmarks like AIME. The multimodal performance of the model is closer to Gemma 4 12B and other current open-weight models. Apertus 1.5 multimodal performance is competitive in spatial understanding among open-weight models and document/chart understanding among open-source models. We also thank our collaborators and external evaluators for their help with efficient benchmarking of Apertus capabilities. In particular, Apertus 1.5 shows strong performance on Swiss Legal tasks, being on par with leading open-weight models (blog, code). Additionally, on Last Translation Benchmark, Apertus 1.5 8B presents machine translation capabilities which match GPT-OSS-20B and outperform models like Claude Haiku 4.5 or Google Translate (leaderboard, paper). Apertus collaborates with the EvalEval Coalition to ensure fully transparent and reproducible benchmarking. We publish the Apertus 1.5 results in the Every Eva