Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesMoonshot v1
43
Winner · 4/8 categories1-bit Bonsai 1.7B· Moonshot v1
Pick Moonshot v1 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.
Moonshot v1 is clearly ahead on the aggregate, 43 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Moonshot v1's sharpest advantage is in knowledge, where it averages 42.9 against 20.7. The single biggest benchmark swing on the page is GPQA, 20.7% to 52%.
Moonshot v1 gives you the larger context window at 128K, 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 | Moonshot v1 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 39% |
| BrowseComp | — | 49% |
| Coding | ||
| HumanEval | — | 45% |
| SWE-bench Verified | — | 34% |
| LiveCodeBench | — | 21% |
| SWE-bench Pro | — | 30% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 49% |
| OfficeQA Pro | — | 57% |
| ReasoningMoonshot v1 wins | ||
| MuSR | 45.1% | 49% |
| BBH | — | 73% |
| LongBench v2 | — | 58% |
| MRCRv2 | — | 56% |
| KnowledgeMoonshot v1 wins | ||
| GPQA | 20.7% | 52% |
| MMLU | — | 53% |
| SuperGPQA | — | 50% |
| MMLU-Pro | — | 64% |
| HLE | — | 5% |
| FrontierScience | — | 49% |
| SimpleQA | — | 51% |
| Instruction FollowingMoonshot v1 wins | ||
| IFEval | 63% | 77% |
| Multilingual | ||
| MGSM | — | 73% |
| MMLU-ProX | — | 68% |
| MathematicsMoonshot v1 wins | ||
| MATH-500 | 34.4% | — |
| AIME 2023 | — | 53% |
| AIME 2024 | — | 55% |
| AIME 2025 | — | 54% |
| HMMT Feb 2023 | — | 49% |
| HMMT Feb 2024 | — | 51% |
| HMMT Feb 2025 | — | 50% |
| BRUMO 2025 | — | 52% |
Moonshot v1 is ahead overall, 43 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 52%.
Moonshot v1 has the edge for knowledge tasks in this comparison, averaging 42.9 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Moonshot v1 has the edge for math in this comparison, averaging 53.1 versus 34.4. 1-bit Bonsai 1.7B stays close enough that the answer can still flip depending on your workload.
Moonshot v1 has the edge for reasoning in this comparison, averaging 54.9 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Moonshot v1 has the edge for instruction following in this comparison, averaging 77 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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