Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesQwen2.5-1M
62
Winner · 4/8 categories1-bit Bonsai 1.7B· Qwen2.5-1M
Pick Qwen2.5-1M if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen2.5-1M is clearly ahead on the aggregate, 62 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen2.5-1M's sharpest advantage is in mathematics, where it averages 84.6 against 34.4. The single biggest benchmark swing on the page is GPQA, 20.7% to 83%.
Qwen2.5-1M gives you the larger context window at 1M, 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 | Qwen2.5-1M |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 65% |
| BrowseComp | — | 72% |
| OSWorld-Verified | — | 59% |
| Coding | ||
| HumanEval | — | 76% |
| SWE-bench Verified | — | 47% |
| LiveCodeBench | — | 40% |
| SWE-bench Pro | — | 49% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 63% |
| OfficeQA Pro | — | 75% |
| ReasoningQwen2.5-1M wins | ||
| MuSR | 45.1% | 79% |
| BBH | — | 82% |
| LongBench v2 | — | 82% |
| MRCRv2 | — | 81% |
| KnowledgeQwen2.5-1M wins | ||
| GPQA | 20.7% | 83% |
| MMLU | — | 84% |
| SuperGPQA | — | 81% |
| MMLU-Pro | — | 74% |
| HLE | — | 10% |
| FrontierScience | — | 74% |
| SimpleQA | — | 81% |
| Instruction FollowingQwen2.5-1M wins | ||
| IFEval | 63% | 84% |
| Multilingual | ||
| MGSM | — | 81% |
| MMLU-ProX | — | 80% |
| MathematicsQwen2.5-1M wins | ||
| MATH-500 | 34.4% | 83% |
| AIME 2023 | — | 85% |
| AIME 2024 | — | 87% |
| AIME 2025 | — | 86% |
| HMMT Feb 2023 | — | 81% |
| HMMT Feb 2024 | — | 83% |
| HMMT Feb 2025 | — | 82% |
| BRUMO 2025 | — | 84% |
Qwen2.5-1M is ahead overall, 62 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 83%.
Qwen2.5-1M has the edge for knowledge tasks in this comparison, averaging 62.1 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for math in this comparison, averaging 84.6 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for reasoning in this comparison, averaging 80.9 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Qwen2.5-1M has the edge for instruction following in this comparison, averaging 84 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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