Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
LFM2-24B-A2B has the cleaner overall profile here, landing at 38 versus 36. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
LFM2-24B-A2B's sharpest advantage is in mathematics, where it averages 50.4 against 45.5. The single biggest benchmark swing on the page is HumanEval, 42 to 29.
GLM-4.5 gives you the larger context window at 128K, compared with 32K for LFM2-24B-A2B.
Pick LFM2-24B-A2B if you want the stronger benchmark profile. GLM-4.5 only becomes the better choice if you need the larger 128K context window.
LFM2-24B-A2B
33.4
GLM-4.5
31.3
LFM2-24B-A2B
18
GLM-4.5
14.4
LFM2-24B-A2B
41.7
GLM-4.5
41
LFM2-24B-A2B
46.6
GLM-4.5
43.8
LFM2-24B-A2B
35.6
GLM-4.5
32
LFM2-24B-A2B
68
GLM-4.5
68
LFM2-24B-A2B
61.4
GLM-4.5
58.1
LFM2-24B-A2B
50.4
GLM-4.5
45.5
LFM2-24B-A2B is ahead overall, 38 to 36. The biggest single separator in this matchup is HumanEval, where the scores are 42 and 29.
LFM2-24B-A2B has the edge for knowledge tasks in this comparison, averaging 35.6 versus 32. Inside this category, MMLU is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for coding in this comparison, averaging 18 versus 14.4. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for math in this comparison, averaging 50.4 versus 45.5. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for reasoning in this comparison, averaging 46.6 versus 43.8. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for agentic tasks in this comparison, averaging 33.4 versus 31.3. Inside this category, OSWorld-Verified is the benchmark that creates the most daylight between them.
LFM2-24B-A2B has the edge for multimodal and grounded tasks in this comparison, averaging 41.7 versus 41. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2-24B-A2B and GLM-4.5 are effectively tied for instruction following here, both landing at 68 on average.
LFM2-24B-A2B has the edge for multilingual tasks in this comparison, averaging 61.4 versus 58.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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