Bijan Bowen compares binary, ternary and full-precision versions of Prism ML's Bonsai 27B, a compressed Qwen 3.6 27B model. The smallest variant runs from a phone, while the larger versions trade memory use for better output quality.
Bijan Bowen finds a consistent capability gradient. Full precision produces the strongest working visual builds, ternary retains much of the reasoning but introduces more implementation errors, and binary remains surprisingly coherent while losing precision and detail.
Bijan Bowen's repository-analysis test shows all three variants understanding cross-file behavior, with lower precision mainly eroding citation accuracy and instruction adherence. The results suggest that aggressive compression can make capable local AI practical, provided users accept slower generation and less reliable execution.
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