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Update
What changedIn the developer’s comparison, the compact Bonsai model was correct on 95.4% of answers that finished, against 96.1% for the larger Qwen model. The figures add a useful qualification to the near-match: the scores were close, but not identical.
The models also diverged on individual problems. Bonsai solved six questions that Qwen missed, while Qwen solved seven that Bonsai missed. Their similar totals therefore conceal different strengths on this set.
Those figures come from witcheer’s test of 63 questions drawn from this year’s AIME and HMMT, using a 32,000-token thinking budget. They are results from one developer-run comparison, not a general measure of either model’s ability.
Sources and evidence- round 2 for the Bonsai files: this year's AIME and HMMT, 63 problems, 32k thinking budget, my RTX 5090. >Ternary-Bonsai-2-27B PQ20, 7.2 GB: 46 >Qwen3.8-27B Q6K, 22.9 GB: 47 >Ternar: Witcheer reports that, among completed answers in a 63-question AIME and HMMT test, the Bonsai model was correct 95.4% of the time and Qwen 96.1%; Bonsai solved six questions Qwen missed, while Qwen solved seven Bonsai missed.
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