Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesLlama 4 Maverick
39
4/8 categories1-bit Bonsai 1.7B· Llama 4 Maverick
Treat this as a split decision. 1-bit Bonsai 1.7B makes more sense if its workflow fits your team better; Llama 4 Maverick is the better fit if knowledge is the priority or you need the larger 1M context window.
1-bit Bonsai 1.7B and Llama 4 Maverick finish on the same overall score, so this is less about a single winner and more about where the edge shows up. The headline says tie; the benchmark table is where the real choice happens.
Llama 4 Maverick gives you the larger context window at 1M, 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 | Llama 4 Maverick |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 37% |
| BrowseComp | — | 51% |
| OSWorld-Verified | — | 38% |
| Coding | ||
| HumanEval | — | 38% |
| SWE-bench Verified | — | 13% |
| LiveCodeBench | — | 15% |
| SWE-bench Pro | — | 17% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 59% |
| OfficeQA Pro | — | 54% |
| ReasoningLlama 4 Maverick wins | ||
| MuSR | 45.1% | 42% |
| BBH | — | 63% |
| LongBench v2 | — | 63% |
| MRCRv2 | — | 63% |
| KnowledgeLlama 4 Maverick wins | ||
| GPQA | 20.7% | 45% |
| MMLU | — | 46% |
| SuperGPQA | — | 43% |
| MMLU-Pro | — | 53% |
| HLE | — | 4% |
| FrontierScience | — | 45% |
| SimpleQA | — | 44% |
| Instruction FollowingLlama 4 Maverick wins | ||
| IFEval | 63% | 68% |
| Multilingual | ||
| MGSM | — | 63% |
| MMLU-ProX | — | 58% |
| MathematicsLlama 4 Maverick wins | ||
| MATH-500 | 34.4% | 59% |
| AIME 2023 | — | 46% |
| AIME 2024 | — | 48% |
| AIME 2025 | — | 47% |
| HMMT Feb 2023 | — | 42% |
| HMMT Feb 2024 | — | 44% |
| HMMT Feb 2025 | — | 43% |
| BRUMO 2025 | — | 45% |
1-bit Bonsai 1.7B and Llama 4 Maverick are tied on overall score, so the right pick depends on which category matters most for your use case.
Llama 4 Maverick has the edge for knowledge tasks in this comparison, averaging 37 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for math in this comparison, averaging 49.3 versus 34.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for reasoning in this comparison, averaging 57.4 versus 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
Llama 4 Maverick has the edge for instruction following in this comparison, averaging 68 versus 63. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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