Bonsai 27B (PrismML)
B Tier · 7.9/10
The first 27B-class model that runs on a phone (2026-07-14) -- ternary and 1-bit quantizations of Qwen3.6 27B squeeze a multimodal, tool-calling, 262K-context model into 3.9-5.9GB under Apache 2.0
Score Breakdown
Benchmark Scores
Benchmarks for Bonsai 27B (ternary + 1-bit builds of Qwen3.6 27B) -- vendor-published aggregate across 15 benchmarks vs the full-precision baseline (85.0)
| Benchmark | Description | Score | |
|---|---|---|---|
| Aggregate (Ternary, 15-benchmark avg) | 80.5 | ||
| Aggregate (1-bit, 15-benchmark avg) | 76.1 | ||
| Quality retention vs baseline (Ternary) | 95% | ||
| Quality retention vs baseline (1-bit) | 90% |
Last updated: 2026-07-18
The Good and the Bad
What we like
- +Genuine first: a 27B-class model running on a phone -- the 1-bit build is 3.9GB, small enough for recent iPhones via MLX
- +Keeps the grown-up capabilities: multi-step reasoning, structured tool calls, vision input (compact 4-bit vision tower), and computer-use agentic loops -- not just chat
- +262K context with speculative decoding support (DSpark drafter) -- unusually long for an on-device model
- +Quality retention is honestly published: ternary keeps ~95% of the Qwen3.6-27B baseline (80.5 vs 85.0 across 15 benchmarks), 1-bit keeps ~90% (76.1)
- +Apache 2.0 with zero usage friction -- weights on Hugging Face, free API preview, Together.ai hosting
What could be better
- −It is a quantization of Qwen3.6 27B, not a new base model -- capability ceiling is the Qwen baseline minus the quantization tax
- −The 5-10% quality loss is real and shows up most on hard reasoning and edge-case coding -- for desktop-class hardware, run the full-precision Qwen3.6-27B instead
- −PrismML is a new/unproven vendor -- long-term maintenance and update cadence are unknowns
- −Phone thermals and battery limit sustained agentic loops in practice
- −Free API is explicitly a limited-time preview -- no published pricing for what comes after
Pricing
Self-hosted (Free)
- ✓Apache 2.0 -- commercial use allowed
- ✓Ternary variant: 5.9GB ({-1,0,+1} weights, 1.71 effective bits/weight, ~95% of baseline quality)
- ✓1-bit variant: 3.9GB ({-1,+1} weights, 1.125 effective bits/weight, ~90% of baseline quality)
- ✓Native MLX (Mac/iPhone/iPad) + CUDA (NVIDIA) support
Locally AI iOS app
- ✓Ships inside the 'Locally AI (Local AI Chat)' iOS app -- on-device, no server round-trip
API preview
- ✓Free limited-time developer preview API
- ✓Also served via Together.ai
System Requirements
Hardware needed to self-host. Min = smallest viable setup (usually heavy quantization). Max = full-precision / production-grade.
| Model variant | Min | Max |
|---|---|---|
| Bonsai 27B 1-bit (3.9GB)Ternary build (5.9GB) needs a bit more headroom; both run fully on-device | Recent iPhone / iPad / Apple Silicon Mac via MLX (ships in the Locally AI iOS app) | Any NVIDIA GPU via CUDA |
Known Issues
- LAUNCH (2026-07-14): PrismML released Bonsai 27B -- 'the first 27B-class model to run on a phone.' Two builds of Qwen3.6 27B: **ternary** (5.9GB, 1.71 effective bits/weight, retains ~95% of baseline -- 80.5 vs 85.0 aggregate across 15 math/coding/tool-calling/knowledge/vision/instruction benchmarks) and **1-bit** (3.9GB, 1.125 bits/weight, ~90% retention, 76.1 aggregate). Multimodal via a compact 4-bit vision tower; 262K context; DSpark speculative drafter; multi-step reasoning, structured tool calls, and computer-use loops preserved. Runs natively on Apple devices via MLX and NVIDIA via CUDA; ships in the 'Locally AI' iOS app; free limited-time developer API plus Together.ai hosting. Apache 2.0. Community reception: Hacker News + r/LocalLLaMA front pagesSource: PrismML (prismml.com/news/bonsai-27b), HuggingFace (prism-ml/Bonsai-27B-mlx-1bit), Hacker News (item 48910545), r/LocalLLaMA · 2026-07-14
Best for
On-device AI builders and privacy-first users who want real reasoning, vision, and tool-calling on a phone or fanless laptop -- and local-AI hobbyists who want the best capability-per-gigabyte available.
Not for
Anyone with a desktop GPU (run the full-precision Qwen3.6-27B or a bigger open model instead) or production workloads that can't tolerate the quantization quality tax and an unproven vendor.
Our Verdict
Bonsai 27B is the most interesting local-AI release of the summer: it moves the 'runs on a phone' frontier from 3-8B toys to a genuine 27B-class model with vision, tool calls, and 262K context, and it publishes its own quality losses instead of hiding them. The honest framing matters -- you're getting ~90-95% of Qwen3.6-27B, which was already the open-weights coding champion in its size class, in 3.9-5.9GB. If you build on-device AI experiences or just want capable private inference in your pocket, this is the new reference point. If you have real hardware, the full-precision base model remains the better tool.
Sources
- PrismML: Bonsai 27B announcement (2026-07-14) (accessed 2026-07-18)
- HuggingFace: prism-ml/Bonsai-27B-mlx-1bit (accessed 2026-07-18)
- Hacker News discussion (accessed 2026-07-18)
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