Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
1/8 categoriesDeepSeek-R1
45
Winner · 3/8 categories1-bit Bonsai 1.7B· DeepSeek-R1
Pick DeepSeek-R1 if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if reasoning is the priority or you want the cheaper token bill.
DeepSeek-R1 is clearly ahead on the aggregate, 45 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek-R1's sharpest advantage is in knowledge, where it averages 47 against 20.7. The single biggest benchmark swing on the page is MATH-500, 34.4% to 97.3%. 1-bit Bonsai 1.7B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
DeepSeek-R1 is also the more expensive model on tokens at $0.55 input / $2.19 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for 1-bit Bonsai 1.7B. That is roughly Infinityx on output cost alone. DeepSeek-R1 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. DeepSeek-R1 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 | DeepSeek-R1 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 42% |
| BrowseComp | — | 49% |
| OSWorld-Verified | — | 44% |
| Coding | ||
| HumanEval | — | 92% |
| SWE-bench Verified | — | 49.2% |
| LiveCodeBench | — | 19% |
| SWE-bench Pro | — | 25% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 43% |
| OfficeQA Pro | — | 53% |
| Reasoning1-bit Bonsai 1.7B wins | ||
| MuSR | 45.1% | 40% |
| BBH | — | 66% |
| LongBench v2 | — | 58% |
| MRCRv2 | — | 57% |
| ARC-AGI-2 | — | 1.3% |
| KnowledgeDeepSeek-R1 wins | ||
| GPQA | 20.7% | 71.5% |
| MMLU | — | 90.8% |
| SuperGPQA | — | 41% |
| MMLU-Pro | — | 84% |
| HLE | — | 14% |
| FrontierScience | — | 44% |
| SimpleQA | — | 30.1% |
| Instruction FollowingDeepSeek-R1 wins | ||
| IFEval | 63% | 83.3% |
| Multilingual | ||
| MGSM | — | 61% |
| MMLU-ProX | — | 60% |
| MathematicsDeepSeek-R1 wins | ||
| MATH-500 | 34.4% | 97.3% |
| AIME 2023 | — | 44% |
| AIME 2024 | — | 79.8% |
| AIME 2025 | — | 45% |
| HMMT Feb 2023 | — | 40% |
| HMMT Feb 2024 | — | 42% |
| HMMT Feb 2025 | — | 41% |
| BRUMO 2025 | — | 43% |
DeepSeek-R1 is ahead overall, 45 to 39. The biggest single separator in this matchup is MATH-500, where the scores are 34.4% and 97.3%.
DeepSeek-R1 has the edge for knowledge tasks in this comparison, averaging 47 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for math in this comparison, averaging 57.4 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
1-bit Bonsai 1.7B has the edge for reasoning in this comparison, averaging 45.1 versus 40. Inside this category, MuSR is the benchmark that creates the most daylight between them.
DeepSeek-R1 has the edge for instruction following in this comparison, averaging 83.3 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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