Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
Winner · 2/8 categoriesGLM-4.5
41
2/8 categories1-bit Bonsai 4B· GLM-4.5
Pick 1-bit Bonsai 4B if you want the stronger benchmark profile. GLM-4.5 only becomes the better choice if reasoning is the priority or you need the larger 128K context window.
1-bit Bonsai 4B has the cleaner overall profile here, landing at 44 versus 41. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
1-bit Bonsai 4B's sharpest advantage is in mathematics, where it averages 65.8 against 42.1. The single biggest benchmark swing on the page is MATH-500, 65.8% to 57%. GLM-4.5 does hit back in reasoning, so the answer changes if that is the part of the workload you care about most.
GLM-4.5 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 | GLM-4.5 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 28% |
| BrowseComp | — | 37% |
| OSWorld-Verified | — | 31% |
| Coding | ||
| HumanEval | — | 29% |
| SWE-bench Verified | — | 18% |
| LiveCodeBench | — | 13% |
| SWE-bench Pro | — | 15% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 36% |
| OfficeQA Pro | — | 47% |
| ReasoningGLM-4.5 wins | ||
| MuSR | 41.4% | 33% |
| BBH | — | 61% |
| LongBench v2 | — | 48% |
| MRCRv2 | — | 52% |
| KnowledgeGLM-4.5 wins | ||
| GPQA | 28.7% | 36% |
| MMLU | — | 37% |
| SuperGPQA | — | 34% |
| MMLU-Pro | — | 51% |
| HLE | — | 3% |
| FrontierScience | — | 40% |
| SimpleQA | — | 35% |
| Instruction Following1-bit Bonsai 4B wins | ||
| IFEval | 69.6% | 68% |
| Multilingual | ||
| MGSM | — | 60% |
| MMLU-ProX | — | 57% |
| Mathematics1-bit Bonsai 4B wins | ||
| MATH-500 | 65.8% | 57% |
| AIME 2023 | — | 37% |
| AIME 2024 | — | 39% |
| AIME 2025 | — | 38% |
| HMMT Feb 2023 | — | 33% |
| HMMT Feb 2024 | — | 35% |
| HMMT Feb 2025 | — | 34% |
| BRUMO 2025 | — | 36% |
1-bit Bonsai 4B is ahead overall, 44 to 41. The biggest single separator in this matchup is MATH-500, where the scores are 65.8% and 57%.
GLM-4.5 has the edge for knowledge tasks in this comparison, averaging 32.1 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 42.1. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for reasoning in this comparison, averaging 45.3 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
1-bit Bonsai 4B has the edge for instruction following in this comparison, averaging 69.6 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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