Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.5 has the cleaner overall profile here, landing at 36 versus 33. 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 multimodal & grounded, where it averages 41 against 32.4. The single biggest benchmark swing on the page is HumanEval, 29 to 17. LFM2.5-1.2B-Thinking does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
LFM2.5-1.2B-Thinking is the reasoning model in the pair, while GLM-4.5 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-4.5 gives you the larger context window at 128K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick GLM-4.5 if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if instruction following is the priority or you want the stronger reasoning-first profile.
GLM-4.5
31.3
LFM2.5-1.2B-Thinking
34.1
GLM-4.5
14.4
LFM2.5-1.2B-Thinking
8.2
GLM-4.5
41
LFM2.5-1.2B-Thinking
32.4
GLM-4.5
43.8
LFM2.5-1.2B-Thinking
38.4
GLM-4.5
32
LFM2.5-1.2B-Thinking
27
GLM-4.5
68
LFM2.5-1.2B-Thinking
72
GLM-4.5
58.1
LFM2.5-1.2B-Thinking
60.7
GLM-4.5
45.5
LFM2.5-1.2B-Thinking
42.3
GLM-4.5 is ahead overall, 36 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 29 and 17.
GLM-4.5 has the edge for knowledge tasks in this comparison, averaging 32 versus 27. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for coding in this comparison, averaging 14.4 versus 8.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for math in this comparison, averaging 45.5 versus 42.3. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for reasoning in this comparison, averaging 43.8 versus 38.4. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for agentic tasks in this comparison, averaging 34.1 versus 31.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for multimodal and grounded tasks in this comparison, averaging 41 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for instruction following in this comparison, averaging 72 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Thinking has the edge for multilingual tasks in this comparison, averaging 60.7 versus 58.1. Inside this category, MMLU-ProX is the benchmark that creates the most daylight between them.
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