Agents
bonsai's document reveal how much cherry picked their headlines are
bonsai claim 98.2% intelligent retained, but their own documents show Ternary Bonsai 2 27B reaches 52.8 and 60.8, respectively, compared with 69.7 and 80.6 for Qwen3.5-27B, retaining roughly three qua
bonsai claim 98.2% intelligent retained, but their own documents show Ternary Bonsai 2 27B reaches 52.8 and 60.8, respectively, compared with 69.7 and 80.6 for Qwen3.5-27B, retaining roughly three quarters of the full-precision performance on both benchmarks. that qwen3.5 is a typo cause these are qwen3.8 numbers, altho qwen3.5 numbers are bonsai_2/q3.5 52.8 / 41.6 = 126.9% bonsai_2/q3.5 60.8 / 72.4 = 84.0% Long-context and coding performance. This release also delivers on the roadmap set out in our initial Bonsai 27B release [2], where we identified long-horizon, tool-driven software engineering as the next major capability to improve. With Ternary Bonsai 2 27B, that progress now shows up directly in agentic performance. Evaluated for the first time on Terminal-Bench 2.1 and SWE-bench Verified, the Ternary Bonsai 2 27B reaches 52.8 and 60.8, respectively, compared with 69.7 and 80.6 for Qwen3.5-27B, retaining roughly three quarters of the full-precision performance on both benchmarks. link to their whitepaper on github, it is on page 7 also 3.8 35b qwhen? plsss submitted by /u/KURD_1_STAN [link] [comments]
Related
- Is ternary (1.58-bit) LLMs making a come back?
- Qwen3.8-27B took a serious hit to knowledge vs 3.6
- Qwen3.8-27B Q6 is a beast at agentic coding
Source: r/LocalLLaMA | 2026-09-18