Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categoriesQwen2.5-VL-32B
~50
1/8 categories1-bit Bonsai 8B· Qwen2.5-VL-32B
Treat this as a split decision. 1-bit Bonsai 8B makes more sense if you need the larger 64K context window; Qwen2.5-VL-32B is the better fit if knowledge is the priority.
1-bit Bonsai 8B and Qwen2.5-VL-32B finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
1-bit Bonsai 8B gives you the larger context window at 64K, compared with 32K for Qwen2.5-VL-32B.
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 8B | Qwen2.5-VL-32B |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| HumanEval | — | 91.5% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 49.5% |
| Reasoning | ||
| MuSR | 50% | — |
| KnowledgeQwen2.5-VL-32B wins | ||
| GPQA | 30% | 46% |
| MMLU-Pro | — | 68.8% |
| Instruction Following | ||
| IFEval | 79.8% | — |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | 66% | — |
1-bit Bonsai 8B and Qwen2.5-VL-32B are tied on overall score, so the right pick depends on which category matters most for your use case.
Qwen2.5-VL-32B has the edge for knowledge tasks in this comparison, averaging 60.8 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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