Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesQwen3.5-27B
71
Winner · 3/8 categories1-bit Bonsai 1.7B· Qwen3.5-27B
Pick Qwen3.5-27B if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.5-27B is clearly ahead on the aggregate, 71 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 80.6 against 20.7. The single biggest benchmark swing on the page is GPQA, 20.7% to 85.5%.
Qwen3.5-27B is the reasoning model in the pair, while 1-bit Bonsai 1.7B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.5-27B gives you the larger context window at 262K, compared with 32K for 1-bit Bonsai 1.7B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | 1-bit Bonsai 1.7B | Qwen3.5-27B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 41.6% |
| BrowseComp | — | 61% |
| OSWorld-Verified | — | 56.2% |
| tau2-bench | — | 79% |
| Coding | ||
| SWE-bench Verified | — | 72.4% |
| LiveCodeBench | — | 80.7% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 75% |
| ReasoningQwen3.5-27B wins | ||
| MuSR | 45.1% | — |
| LongBench v2 | — | 60.6% |
| KnowledgeQwen3.5-27B wins | ||
| GPQA | 20.7% | 85.5% |
| MMLU-Pro | — | 86.1% |
| SuperGPQA | — | 65.6% |
| Instruction FollowingQwen3.5-27B wins | ||
| IFEval | 63% | 95% |
| Multilingual | ||
| MMLU-ProX | — | 82.2% |
| Mathematics | ||
| MATH-500 | 34.4% | — |
Qwen3.5-27B is ahead overall, 71 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 85.5%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 45.1. 1-bit Bonsai 1.7B stays close enough that the answer can still flip depending on your workload.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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