Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 1.7B
~39
0/8 categoriesGLM-4.5
41
Winner · 4/8 categories1-bit Bonsai 1.7B· GLM-4.5
Pick GLM-4.5 if you want the stronger benchmark profile. 1-bit Bonsai 1.7B only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-4.5 has the cleaner overall profile here, landing at 41 versus 39. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GLM-4.5's sharpest advantage is in knowledge, where it averages 32.1 against 20.7. The single biggest benchmark swing on the page is MATH-500, 34.4% to 57%.
GLM-4.5 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 | 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 | 45.1% | 33% |
| BBH | — | 61% |
| LongBench v2 | — | 48% |
| MRCRv2 | — | 52% |
| KnowledgeGLM-4.5 wins | ||
| GPQA | 20.7% | 36% |
| MMLU | — | 37% |
| SuperGPQA | — | 34% |
| MMLU-Pro | — | 51% |
| HLE | — | 3% |
| FrontierScience | — | 40% |
| SimpleQA | — | 35% |
| Instruction FollowingGLM-4.5 wins | ||
| IFEval | 63% | 68% |
| Multilingual | ||
| MGSM | — | 60% |
| MMLU-ProX | — | 57% |
| MathematicsGLM-4.5 wins | ||
| MATH-500 | 34.4% | 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% |
GLM-4.5 is ahead overall, 41 to 39. The biggest single separator in this matchup is MATH-500, where the scores are 34.4% and 57%.
GLM-4.5 has the edge for knowledge tasks in this comparison, averaging 32.1 versus 20.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for math in this comparison, averaging 42.1 versus 34.4. 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 45.1. Inside this category, MuSR is the benchmark that creates the most daylight between them.
GLM-4.5 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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