Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
1-bit Bonsai 8B
~50
0/8 categoriesGLM-4.7
72
Winner · 4/8 categories1-bit Bonsai 8B· GLM-4.7
Pick GLM-4.7 if you want the stronger benchmark profile. 1-bit Bonsai 8B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7 is clearly ahead on the aggregate, 72 to 50. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in knowledge, where it averages 63.3 against 30. The single biggest benchmark swing on the page is GPQA, 30% to 85.7%.
GLM-4.7 is the reasoning model in the pair, while 1-bit Bonsai 8B 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-4.7 gives you the larger context window at 200K, compared with 64K for 1-bit Bonsai 8B.
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 8B | GLM-4.7 |
|---|---|---|
| Agentic | ||
| Terminal-Bench 2.0 | — | 41% |
| BrowseComp | — | 52% |
| OSWorld-Verified | — | 61% |
| Coding | ||
| HumanEval | — | 94.2% |
| SWE-bench Verified | — | 73.8% |
| LiveCodeBench | — | 84.9% |
| SWE-bench Pro | — | 51% |
| Multimodal & Grounded | ||
| MMMU-Pro | — | 66% |
| OfficeQA Pro | — | 76% |
| ReasoningGLM-4.7 wins | ||
| MuSR | 50% | 80% |
| BBH | — | 84% |
| LongBench v2 | — | 79% |
| MRCRv2 | — | 78% |
| KnowledgeGLM-4.7 wins | ||
| GPQA | 30% | 85.7% |
| MMLU | — | 86% |
| SuperGPQA | — | 82% |
| MMLU-Pro | — | 84.3% |
| HLE | — | 24.8% |
| FrontierScience | — | 72% |
| SimpleQA | — | 46% |
| Instruction FollowingGLM-4.7 wins | ||
| IFEval | 79.8% | 88% |
| Multilingual | ||
| MGSM | — | 94% |
| MMLU-ProX | — | 78% |
| MathematicsGLM-4.7 wins | ||
| MATH-500 | 66% | 85% |
| AIME 2023 | — | 86% |
| AIME 2024 | — | 88% |
| AIME 2025 | — | 95.7% |
| HMMT Feb 2023 | — | 82% |
| HMMT Feb 2024 | — | 84% |
| HMMT Feb 2025 | — | 97.1% |
| BRUMO 2025 | — | 85% |
GLM-4.7 is ahead overall, 72 to 50. The biggest single separator in this matchup is GPQA, where the scores are 30% and 85.7%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 63.3 versus 30. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for math in this comparison, averaging 89.3 versus 66. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for reasoning in this comparison, averaging 78.9 versus 50. Inside this category, MuSR is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for instruction following in this comparison, averaging 88 versus 79.8. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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