PrismML

Today, we’re releasing Bonsai 2 2B, 4B, and 9B.

Based on Qwen3.5, these new ternary models bring stronger reasoning, visual understanding, and tool calling to compact footprints designed for everyday devices:

• 2B: 0.43GB
• 4B: 0.95GB
• 9B: 2.0GB

For this release, we focused on what makes small models useful as local assistants: following instructions, avoiding repetitive loops, calling tools on mobile devices, and giving useful answers without unnecessary output. The 9B model uses roughly 40% fewer tokens on standard instruction-following and everyday math tasks. Solving the Riemann hypothesis can wait.

These models are successors to our first-generation Bonsai 1.7B, 4B, and 8B, and provide a foundation for bringing practical agentic functionality to local applications on personal computers, phones, and wearables.

The Bonsai 2 family now includes four sizes spanning 2B to 27B parameters. Developers can choose the balance of capability, memory use, and responsiveness that fits their application and hardware.

Available today under Apache 2.0.

We have been very excited to see how the community has been utilizing our Bonsai 2 model, well beyond use cases that we had originally envisioned it for. We’ve spent the last few days putting Bonsai 2 27B through various examples inspired by what the community has shown us.

These examples showcase the strengths, and some of the shortcomings–particularly on longer multi-turn agentic workflows, and will be extremely helpful as we continue to improve.

We’ll be sharing a few of those demos, along with some practical guidance on the settings and prompting that get the best results from the model.

Demo repo: github.com/PrismML-Eng/Bo…

Great to see @TechCrunch cover our work with @Qualcomm!

Read here: techcrunch.com/2026/09/24/pri…

Today at Snapdragon Summit, we’re announcing 1-bit Bonsai vision-language model running locally on AI smart glasses powered by Snapdragon® AR1 Gen 1 Platform.

With Qualcomm Technologies, we optimized Bonsai for Snapdragon Hexagon™ NPU, delivering 4x lower memory usage and 2x faster token generation for equivalent intelligence, making it possible to bring significantly more capable AI to run directly on highly constrained wearable devices.

We believe model-hardware co-design will be critical to bringing increasingly capable AI from the cloud onto the devices people use every day.

Today, we’re announcing Ternary Bonsai 2 27B.

Based on Qwen3.8 27B, Bonsai 2 27B is 9x smaller than its full-precision counterpart while retaining 98.2% of its aggregate benchmark performance.

Two months after the first Bonsai 27B release, the biggest change is quality. The footprint remains 5.9 GB, but the gap to full precision has narrowed materially, with particularly strong gains in agentic coding, multimodal reasoning, and long-horizon tool use.

Ternary Bonsai 2 27B is available today under Apache 2.0.