Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 4B
~44
0/8 categoriesGLM-5 (Reasoning)
82
Winner · 4/8 categories1-bit Bonsai 4B· GLM-5 (Reasoning)
Pick GLM-5 (Reasoning) if you want the stronger benchmark profile. 1-bit Bonsai 4B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 82 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 (Reasoning)'s sharpest advantage is in reasoning, where it averages 87.4 against 41.4. The single biggest benchmark swing on the page is GPQA, 28.7% to 94%.
GLM-5 (Reasoning) is the reasoning model in the pair, while 1-bit Bonsai 4B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GLM-5 (Reasoning) gives you the larger context window at 200K, 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-5 (Reasoning) |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 81% |
| BrowseComp | — | 80% |
| OSWorld-Verified | — | 74% |
| Coding | ||
| HumanEval | — | 88% |
| SWE-bench Verified | — | 62% |
| LiveCodeBench | — | 58% |
| SWE-bench Pro | — | 67% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 74% |
| OfficeQA Pro | — | 84% |
| ReasoningGLM-5 (Reasoning) wins | ||
| MuSR | 41.4% | 90% |
| BBH | — | 91% |
| LongBench v2 | — | 86% |
| MRCRv2 | — | 87% |
| KnowledgeGLM-5 (Reasoning) wins | ||
| GPQA | 28.7% | 94% |
| MMLU | — | 96% |
| SuperGPQA | — | 92% |
| MMLU-Pro | — | 81% |
| HLE | — | 29% |
| FrontierScience | — | 83% |
| SimpleQA | — | 92% |
| Instruction FollowingGLM-5 (Reasoning) wins | ||
| IFEval | 69.6% | 92% |
| Multilingual | ||
| MGSM | — | 89% |
| MMLU-ProX | — | 85% |
| MathematicsGLM-5 (Reasoning) wins | ||
| MATH-500 | 65.8% | 92% |
| AIME 2023 | — | 98% |
| AIME 2024 | — | 99% |
| AIME 2025 | — | 98% |
| HMMT Feb 2023 | — | 94% |
| HMMT Feb 2024 | — | 96% |
| HMMT Feb 2025 | — | 95% |
| BRUMO 2025 | — | 96% |
GLM-5 (Reasoning) is ahead overall, 82 to 44. The biggest single separator in this matchup is GPQA, where the scores are 28.7% and 94%.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 73.7 versus 28.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for math in this comparison, averaging 95.8 versus 65.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for reasoning in this comparison, averaging 87.4 versus 41.4. Inside this category, MuSR is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for instruction following in this comparison, averaging 92 versus 69.6. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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