Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7
72
Winner · 2/8 categoriesLFM2.5-350M
~39
0/8 categoriesGLM-4.7· LFM2.5-350M
Pick GLM-4.7 if you want the stronger benchmark profile. LFM2.5-350M 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 39. 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 23.8. The single biggest benchmark swing on the page is MMLU-Pro, 84.3% to 20.0%.
GLM-4.7 is the reasoning model in the pair, while LFM2.5-350M 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 32K for LFM2.5-350M.
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 | GLM-4.7 | LFM2.5-350M |
|---|---|---|
| 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% | — |
| Reasoning | ||
| MuSR | 80% | — |
| BBH | 84% | — |
| LongBench v2 | 79% | — |
| MRCRv2 | 78% | — |
| KnowledgeGLM-4.7 wins | ||
| MMLU | 86% | — |
| GPQA | 85.7% | 30.6% |
| SuperGPQA | 82% | — |
| MMLU-Pro | 84.3% | 20.0% |
| HLE | 24.8% | — |
| FrontierScience | 72% | — |
| SimpleQA | 46% | — |
| Instruction FollowingGLM-4.7 wins | ||
| IFEval | 88% | 77.0% |
| Multilingual | ||
| MGSM | 94% | — |
| MMLU-ProX | 78% | — |
| Mathematics | ||
| 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% | — |
| MATH-500 | 85% | — |
GLM-4.7 is ahead overall, 72 to 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 84.3% and 20.0%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 63.3 versus 23.8. Inside this category, MMLU-Pro 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 77. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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