Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
1/8 categoriesDeepSeek V3
51
Winner · 3/8 categories1-bit Bonsai 8B· DeepSeek V3
Pick DeepSeek V3 if you want the stronger benchmark profile. 1-bit Bonsai 8B only becomes the better choice if reasoning is the priority or you want the cheaper token bill.
DeepSeek V3 finishes one point ahead overall, 51 to 50. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
DeepSeek V3's sharpest advantage is in knowledge, where it averages 57.5 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 59.1%. 1-bit Bonsai 8B does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
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 8B. That is roughly Infinityx on output cost alone. DeepSeek V3 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 V3 |
|---|---|---|
| Agentic | ||
| Coming soon | ||
| Coding | ||
| LiveCodeBench | — | 37.6% |
| SWE-bench Verified | — | 42% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning1-bit Bonsai 8B wins | ||
| MuSR | 50% | — |
| LongBench v2 | — | 48.7% |
| KnowledgeDeepSeek V3 wins | ||
| GPQA | 30% | 59.1% |
| MMLU-Pro | — | 75.9% |
| SimpleQA | — | 24.9% |
| Instruction FollowingDeepSeek V3 wins | ||
| IFEval | 79.8% | 86.1% |
| Multilingual | ||
| Coming soon | ||
| MathematicsDeepSeek V3 wins | ||
| MATH-500 | 66% | 90.2% |
| AIME 2024 | — | 39.2% |
DeepSeek V3 is ahead overall, 51 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 59.1%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 57.5 versus 30. 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 66. 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 48.7. DeepSeek V3 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 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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