Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesDeepSeek LLM 2.0
61
Winner · 4/8 categories1-bit Bonsai 1.7B· DeepSeek LLM 2.0
Pick DeepSeek LLM 2.0 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.
DeepSeek LLM 2.0 is clearly ahead on the aggregate, 61 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek LLM 2.0's sharpest advantage is in mathematics, where it averages 80.8 against 34.4. The single biggest benchmark swing on the page is GPQA, 20.7% to 78%.
DeepSeek LLM 2.0 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 LLM 2.0 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 57% |
| BrowseComp | — | 62% |
| OSWorld-Verified | — | 56% |
| Coding | ||
| HumanEval | — | 73% |
| SWE-bench Verified | — | 46% |
| LiveCodeBench | — | 39% |
| SWE-bench Pro | — | 46% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 60% |
| OfficeQA Pro | — | 70% |
| ReasoningDeepSeek LLM 2.0 wins | ||
| MuSR | 45.1% | 75% |
| BBH | — | 81% |
| LongBench v2 | — | 70% |
| MRCRv2 | — | 69% |
| KnowledgeDeepSeek LLM 2.0 wins | ||
| GPQA | 20.7% | 78% |
| MMLU | — | 79% |
| SuperGPQA | — | 76% |
| MMLU-Pro | — | 72% |
| HLE | — | 12% |
| FrontierScience | — | 67% |
| SimpleQA | — | 77% |
| Instruction FollowingDeepSeek LLM 2.0 wins | ||
| IFEval | 63% | 85% |
| Multilingual | ||
| MGSM | — | 82% |
| MMLU-ProX | — | 77% |
| MathematicsDeepSeek LLM 2.0 wins | ||
| MATH-500 | 34.4% | 83% |
| AIME 2023 | — | 80% |
| AIME 2024 | — | 82% |
| AIME 2025 | — | 81% |
| HMMT Feb 2023 | — | 76% |
| HMMT Feb 2024 | — | 78% |
| HMMT Feb 2025 | — | 77% |
| BRUMO 2025 | — | 79% |
DeepSeek LLM 2.0 is ahead overall, 61 to 39. The biggest single separator in this matchup is GPQA, where the scores are 20.7% and 78%.
DeepSeek LLM 2.0 has the edge for knowledge tasks in this comparison, averaging 59.1 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for math in this comparison, averaging 80.8 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for reasoning in this comparison, averaging 71 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
DeepSeek LLM 2.0 has the edge for instruction following in this comparison, averaging 85 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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