Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesDeepSeek V3
51
Winner · 4/8 categories1-bit Bonsai 1.7B· DeepSeek V3
Pick DeepSeek V3 if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if you want the cheaper token bill.
DeepSeek V3 is clearly ahead on the aggregate, 51 to 39. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3's sharpest advantage is in mathematics, where it averages 90.2 against 34.4. The single biggest benchmark swing on the page is MATH-500, 34.4% to 90.2%.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 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 V3 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 V3 |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| LiveCodeBench | — | 37.6% |
| SWE-bench Verified | — | 42% |
| Multimodal & Grounded | ||
| Coming soon | ||
| ReasoningDeepSeek V3 wins | ||
| MuSR | 45.1% | — |
| LongBench v2 | — | 48.7% |
| KnowledgeDeepSeek V3 wins | ||
| GPQA | 20.7% | 59.1% |
| MMLU-Pro | — | 75.9% |
| SimpleQA | — | 24.9% |
| Instruction FollowingDeepSeek V3 wins | ||
| IFEval | 63% | 86.1% |
| Multilingual | ||
| Coming soon | ||
| MathematicsDeepSeek V3 wins | ||
| MATH-500 | 34.4% | 90.2% |
| AIME 2024 | — | 39.2% |
DeepSeek V3 is ahead overall, 51 to 39. The biggest single separator in this matchup is MATH-500, where the scores are 34.4% and 90.2%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 57.5 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for math in this comparison, averaging 90.2 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for reasoning in this comparison, averaging 48.7 versus 45.1. 1-bit Bonsai 1.7B stays close enough that the answer can still flip depending on your workload.
DeepSeek V3 has the edge for instruction following in this comparison, averaging 86.1 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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