Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.5 is clearly ahead on the aggregate, 36 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.5's sharpest advantage is in reasoning, where it averages 43.8 against 32.1. The single biggest benchmark swing on the page is HumanEval, 29 to 14. LFM2.5-1.2B-Instruct 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 LFM2.5-1.2B-Instruct.
Pick GLM-4.5 if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if instruction following is the priority.
GLM-4.5
31.3
LFM2.5-1.2B-Instruct
25.7
GLM-4.5
14.4
LFM2.5-1.2B-Instruct
7.2
GLM-4.5
41
LFM2.5-1.2B-Instruct
32.4
GLM-4.5
43.8
LFM2.5-1.2B-Instruct
32.1
GLM-4.5
32
LFM2.5-1.2B-Instruct
26
GLM-4.5
68
LFM2.5-1.2B-Instruct
80
GLM-4.5
58.1
LFM2.5-1.2B-Instruct
60.7
GLM-4.5
45.5
LFM2.5-1.2B-Instruct
37
GLM-4.5 is ahead overall, 36 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 29 and 14.
GLM-4.5 has the edge for knowledge tasks in this comparison, averaging 32 versus 26. 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 7.2. 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 37. 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 32.1. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GLM-4.5 has the edge for agentic tasks in this comparison, averaging 31.3 versus 25.7. 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-Instruct has the edge for instruction following in this comparison, averaging 80 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
LFM2.5-1.2B-Instruct 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.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.