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Apertus 1.5 Adds Image and Audio Input, a 262,144-Token Context and a Thinking Mode to Its Fully Open 8B and 70B Models

The Apertus project's 1.5 release adds image and audio understanding, an optional thinking mode and a 262,144-token window to its 8B and 70B open models, under Apache 2.0.

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Overview

The Apertus project has published release notes and a technical report for Apertus 1.5, an update to its 8B and 70B models that adds image and audio understanding, an optional thinking mode, a context window four times longer than Apertus 1.0 and improved instruction-following and tool use, according to the project’s release notes on Hugging Face. The release notes are dated October 15, 2026. They state that the updated weights were released to Hugging Face on July 24, 2026 under an Apache 2.0 license. The accompanying report, Apertus 1.5: From Open Text Models to Open Multimodal Reasoners, was submitted to arXiv on October 11, 2026.

What the Release Contains

  • Continued pretraining. According to the release notes, Apertus 1.5 is a continued pretraining of the first-generation models, adding 4 trillion tokens of text and multimodal training data to the 8B model and 2 trillion tokens to the 70B model, for totals of 19T and 17T respectively.
  • Multimodality through the tokenizer. The release notes say images and audio enter the model as discrete tokens in the same sequence as text, in an early-fusion manner, instead of through a separate vision encoder. Images are tokenized with the Emu3.5 tokenizer, so a 512×512 image becomes 1,024 tokens, and audio with WavTokenizer at 40 tokens per second. The project says it only extends the vocabulary, so the architecture is unchanged, and that the models understand images and audio but generate only text. Spoken-language processing is described as experimental.
  • Long context. The context window grows to 262,144 tokens, a 4x increase over Apertus 1.0, according to the release notes, which say the window was extended step by step from 8k to 262k tokens during the final pretraining stage.
  • Thinking mode. The arXiv abstract describes thinking as a flag of the chat template rather than a separate model, and says it lifts mathematical reasoning by up to 20 points on the 8B model.

Training Pipeline

Pretraining runs in four stages, per the release notes: unpaired text, images and audio; paired image-caption and audio-transcript data; a cooldown with more math and code; and long-context extension. More than half of the training tokens are text-only. Text data was refreshed with sources including FineWeb2-HQ+, FinePDFs and Nemotron-CC, and the project says all data is publicly available, permissively licensed, and respects robots.txt opt-outs retroactively.

Post-training has five stages, according to the release notes: supervised fine-tuning, long-context supervised fine-tuning, reinforcement learning with verifiable rewards, DPO preference alignment, and charter alignment using SDPO. Supervised fine-tuning used 3.7M text samples plus image and speech data, and reinforcement learning used about 350k prompts across math, code, instruction-following, multilingual and vision tasks. The project’s table lists the charter-alignment stage as 8B only; the 70B final model is the DPO checkpoint. Intermediate checkpoints for the base, SFT, RLVR and DPO stages are hosted in the project’s Hugging Face collection.

Benchmark Claims

All comparisons below are the developers’ own reporting. The release notes say that, “While still trailing frontier open-weight models,” Apertus 1.5 has significantly improved coding and instruction following, with the 8B outperforming similarly sized open-weight models on instruction-following and multilingual tasks. They place multimodal performance closer to Gemma 4 12B and other current open-weight models. On the Last Translation Benchmark, the notes say the 8B’s machine-translation capability matches GPT-OSS-20B and outperforms Claude Haiku 4.5 and Google Translate.

The arXiv abstract says that among fully open models of its class the 8B leads on knowledge, instruction following and cultural benchmarks. It also says specialized vision and audio models remain ahead on perception and transcription of English and other high-resource languages.

The project publishes results in the Every Eval Ever schema with the EvalEval Coalition. The release notes cite EvalEval’s finding that the schema can represent 56 of the 60 evaluation-reporting elements that frameworks such as Article 55 of the EU AI Act and the GPAI Code of Practice call for.

Availability and Roadmap

According to the release notes, Proton Lumo and Infomaniak Euria have integrated Apertus 1.5, and developer API access has been announced by providers including Swisscom, Infomaniak and Phoeniqs. The notes also say the team announced a roadmap for Apertus 2.0 at the AI+X Summit in Zurich: a sparse or mixture-of-experts architecture of about 600B total parameters, with agents, mathematics and coding as priorities.

What We Don’t Know

  • The benchmark comparisons come from the project’s own publications; the sources reviewed here do not include independent replication.
  • The release notes do not give a release date for Apertus 2.0.
  • The sources do not explain why the release notes appeared on October 15, nearly three months after the July 24 weight release.