GoKawiilToday, we're announcing Bonsai 27B, based on Qwen3.6 27B, the new multimodal flagship of the Bonsai family and the first model of its capability class to run on a phone.
Our earlier releases proved that models with 1-bit and ternary weights could produce commercially useful language models. Bonsai 27B extends that frontier to a new capability tier: multi-step reasoning, structured tool calls, vision tasks, and computer-use agentic loops that stay coherent across many steps. Until today, deploying that tier locally has been impractical for a concrete reason: a 27B model occupies roughly 54GB in 16-bit precision, and even a good 4-bit build, at 18GB, is too large for a phone and for most laptops.
Bonsai 27B changes that. It comes in two variants:
Ternary Bonsai 27B uses ternary {−1, 0, +1} weights with FP16 group-wise scaling, giving a true 1.71 effective bits per weight. At 5.9 GB, it is the quality-oriented variant: it runs on an everyday laptop with the full reasoning, tool-calling, and agentic capability.
1-bit Bonsai 27B uses binary {−1, +1} weights with the same group-wise scaling, giving 1.125 effective bits per weight. At 3.9 GB, it is the footprint-oriented variant, which fits within the memory budget of an iPhone 17 Pro, bringing a 27B-class model onto a phone for the first time.
As with every Bonsai release, the low-bit representation runs end to end across the language network, embeddings, attention, MLPs, and the LM head, with no higher-precision escape hatches. Both variants are multimodal, with the vision tower shipping in a compact 4-bit form so on-device workflows can see screenshots, documents, and camera input, not just text. Bonsai 27B carries a full 262K-token context, and supports speculative-decoding, compounding the speed with lossless draft-and-verify acceleration. Everything is available today under the Apache 2.0 License.
Retaining the intelligence
Across a 15-benchmark suite spanning knowledge, reasoning, math, coding, instruction following, tool calling, and vision (evaluated in thinking mode, where the model's full reasoning is exercised) Ternary Bonsai 27B retains 95% of the full-precision baseline, and 1-bit Bonsai 27B retains 90%.
Category (benchmarks) Qwen 3.6
27B Ternary Bonsai
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