Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
1/8 categoriesLlama 3 70B
45
Winner · 3/8 categories1-bit Bonsai 4B· Llama 3 70B
Pick Llama 3 70B if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if mathematics is the priority.
Llama 3 70B finishes one point ahead overall, 45 to 44. 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.
Llama 3 70B's sharpest advantage is in knowledge, where it averages 55.6 against 28.7. The single biggest benchmark swing on the page is GPQA, 28.7% to 58%. 1-bit Bonsai 4B does hit back in mathematics, so the answer changes if that is the part of the workload you care about most.
Llama 3 70B gives you the larger context window at 128K, compared with 32K for 1-bit Bonsai 4B.
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 4B | Llama 3 70B |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 37% |
| OSWorld-Verified | — | 41% |
| Coding | ||
| HumanEval | — | 50% |
| SWE-bench Verified | — | 9% |
| LiveCodeBench | — | 19% |
| SWE-bench Pro | — | 14% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 50% |
| ReasoningLlama 3 70B wins | ||
| MuSR | 41.4% | 54% |
| BBH | — | 74% |
| LongBench v2 | — | 61% |
| MRCRv2 | — | 61% |
| KnowledgeLlama 3 70B wins | ||
| GPQA | 28.7% | 58% |
| MMLU | — | 58% |
| SuperGPQA | — | 56% |
| MMLU-Pro | — | 55% |
| FrontierScience | — | 54% |
| SimpleQA | — | 56% |
| Instruction FollowingLlama 3 70B wins | ||
| IFEval | 69.6% | 77% |
| Multilingual | ||
| MGSM | — | 72% |
| MMLU-ProX | — | 65% |
| Mathematics1-bit Bonsai 4B wins | ||
| MATH-500 | 65.8% | 71% |
| AIME 2023 | — | 58% |
| AIME 2024 | — | 60% |
| AIME 2025 | — | 59% |
| HMMT Feb 2023 | — | 54% |
| HMMT Feb 2024 | — | 56% |
| HMMT Feb 2025 | — | 55% |
| BRUMO 2025 | — | 57% |
Llama 3 70B is ahead overall, 45 to 44. The biggest single separator in this matchup is GPQA, where the scores are 28.7% and 58%.
Llama 3 70B has the edge for knowledge tasks in this comparison, averaging 55.6 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
1-bit Bonsai 4B has the edge for math in this comparison, averaging 65.8 versus 61.3. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Llama 3 70B has the edge for reasoning in this comparison, averaging 59.1 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Llama 3 70B has the edge for instruction following in this comparison, averaging 77 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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