Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
Winner · 2/8 categoriesDeepSeek-R1
45
2/8 categories1-bit Bonsai 8B· DeepSeek-R1
Pick 1-bit Bonsai 8B if you want the stronger benchmark profile. DeepSeek-R1 only becomes the better choice if knowledge is the priority or you need the larger 128K context window.
1-bit Bonsai 8B is clearly ahead on the aggregate, 50 to 45. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
1-bit Bonsai 8B's sharpest advantage is in reasoning, where it averages 50 against 40. The single biggest benchmark swing on the page is GPQA, 30% to 71.5%. DeepSeek-R1 does hit back in knowledge, 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 8B. That is roughly Infinityx on output cost alone. DeepSeek-R1 is the reasoning model in the pair, while 1-bit Bonsai 8B 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 64K for 1-bit Bonsai 8B.
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 | 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 8B wins | ||
| MuSR | 50% | 40% |
| BBH | — | 66% |
| LongBench v2 | — | 58% |
| MRCRv2 | — | 57% |
| ARC-AGI-2 | — | 1.3% |
| KnowledgeDeepSeek-R1 wins | ||
| GPQA | 30% | 71.5% |
| MMLU | — | 90.8% |
| SuperGPQA | — | 41% |
| MMLU-Pro | — | 84% |
| HLE | — | 14% |
| FrontierScience | — | 44% |
| SimpleQA | — | 30.1% |
| Instruction FollowingDeepSeek-R1 wins | ||
| IFEval | 79.8% | 83.3% |
| Multilingual | ||
| MGSM | — | 61% |
| MMLU-ProX | — | 60% |
| Mathematics1-bit Bonsai 8B wins | ||
| MATH-500 | 66% | 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% |
1-bit Bonsai 8B is ahead overall, 50 to 45. The biggest single separator in this matchup is GPQA, where the scores are 30% and 71.5%.
DeepSeek-R1 has the edge for knowledge tasks in this comparison, averaging 47 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
1-bit Bonsai 8B has the edge for math in this comparison, averaging 66 versus 57.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
1-bit Bonsai 8B has the edge for reasoning in this comparison, averaging 50 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 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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