Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.5
41
Winner · 1/8 categoriesLFM2.5-350M
~39
1/8 categoriesGLM-4.5· LFM2.5-350M
Pick GLM-4.5 if you want the stronger benchmark profile. LFM2.5-350M only becomes the better choice if instruction following is the priority.
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 23.8. The single biggest benchmark swing on the page is MMLU-Pro, 51% to 20.0%. LFM2.5-350M does hit back in instruction following, 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 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-4.5 | LFM2.5-350M |
|---|---|---|
| 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% | — |
| Reasoning | ||
| MuSR | 33% | — |
| BBH | 61% | — |
| LongBench v2 | 48% | — |
| MRCRv2 | 52% | — |
| KnowledgeGLM-4.5 wins | ||
| MMLU | 37% | — |
| GPQA | 36% | 30.6% |
| SuperGPQA | 34% | — |
| MMLU-Pro | 51% | 20.0% |
| HLE | 3% | — |
| FrontierScience | 40% | — |
| SimpleQA | 35% | — |
| Instruction FollowingLFM2.5-350M wins | ||
| IFEval | 68% | 77.0% |
| Multilingual | ||
| MGSM | 60% | — |
| MMLU-ProX | 57% | — |
| Mathematics | ||
| AIME 2023 | 37% | — |
| AIME 2024 | 39% | — |
| AIME 2025 | 38% | — |
| HMMT Feb 2023 | 33% | — |
| HMMT Feb 2024 | 35% | — |
| HMMT Feb 2025 | 34% | — |
| BRUMO 2025 | 36% | — |
| MATH-500 | 57% | — |
GLM-4.5 is ahead overall, 41 to 39. The biggest single separator in this matchup is MMLU-Pro, where the scores are 51% and 20.0%.
GLM-4.5 has the edge for knowledge tasks in this comparison, averaging 32.1 versus 23.8. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
LFM2.5-350M has the edge for instruction following in this comparison, averaging 77 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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