
What happened
Apertus released version 1.5 of its open-source language models on July 24, 2026, with 8B and 70B variants. The update adds image understanding, an optional thinking mode for reasoning, a context window extended to 262,144 tokens (four times longer than Apertus 1.0), and improved instruction-following and tool use. The 8B model received 4 trillion tokens of additional training data and the 70B model received 2 trillion tokens.
Why it matters
Apertus publishes its models as fully open — weights, training data, and methodology included — meaning developers can use and modify the software without commercial licensing restrictions. The multimodal and reasoning enhancements make the models more practical for real-world tasks like document analysis and complex problem-solving, while the longer context window allows the models to work with much larger inputs.
What to watch
A technical report with detailed benchmark results, training pipelines, and intermediate checkpoints will be published in the coming weeks. The models are available on Hugging Face, and the team is working with inference providers to make them available on multiple platforms.
Summaries like this, in your inbox every morning.
Apertus 1.5 represents an incremental but substantive step forward in open-source AI capabilities. The addition of image understanding and reasoning mode—features previously associated with proprietary frontier models—brings practical multimodal and reasoning abilities to fully open models. The four-fold increase in context length (to 262,144 tokens) addresses a concrete constraint for applications requiring longer document processing.
The commitment to open methodology is notable: the announcement promises publication of a technical report with detailed benchmark results, training pipelines, and intermediate checkpoints. This transparency, combined with weights and data being publicly available, sets the release apart from closed-source offerings and enables other teams to build upon and audit the work. The team is actively working to distribute the models across multiple inference platforms, widening accessibility beyond researchers with dedicated hardware.
Pick your industry and the AI tools you use, and get news related to your work every day.
Free · 30 seconds with Google · unsubscribe anytimeWhat is AIToday? →
Ask AI anything about this article. The AI reads this article, earlier AIToday articles, and Wikipedia, and cites its sources. Q&As are published on this page for other readers too.
Google Deepmind's Gemini 4 Argon scored 97.5 percent on ARC-AGI-3 at a cost of $4,800, second only to GPT-6 As…

Cammarata reinterprets InceptionV1's cat-car neuron as a V-shape detector, not two unrelated concepts

Microsoft's Surface Laptop Ultra, using Nvidia's RTX Spark platform, starts at $2,599 (up to $5,899)

Meta's new commercial terms, effective October 30th, appear to let it crawl and train AI on content from websi…

Vercel data published Thursday shows TypeSafe AI's Jev, a lightweight decision model for choosing among predet…

Reflection AI introduced Beam, an open-weight model for agentic coding and long-horizon tasks, with 501 billio…
