Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7 is clearly ahead on the aggregate, 67 to 30. 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 reasoning, where it averages 80.2 against 32.1. The single biggest benchmark swing on the page is HumanEval, 78 to 14.
GLM-4.7 is the reasoning model in the pair, while LFM2.5-1.2B-Instruct 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 LFM2.5-1.2B-Instruct.
Pick GLM-4.7 if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7
66.1
LFM2.5-1.2B-Instruct
25.7
GLM-4.7
46.6
LFM2.5-1.2B-Instruct
7.2
GLM-4.7
70.5
LFM2.5-1.2B-Instruct
32.4
GLM-4.7
80.2
LFM2.5-1.2B-Instruct
32.1
GLM-4.7
61.8
LFM2.5-1.2B-Instruct
26
GLM-4.7
85
LFM2.5-1.2B-Instruct
80
GLM-4.7
79.1
LFM2.5-1.2B-Instruct
60.7
GLM-4.7
85
LFM2.5-1.2B-Instruct
37
GLM-4.7 is ahead overall, 67 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 78 and 14.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 61.8 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 46.6 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for math in this comparison, averaging 85 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for reasoning in this comparison, averaging 80.2 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for agentic tasks in this comparison, averaging 66.1 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for multimodal and grounded tasks in this comparison, averaging 70.5 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for instruction following in this comparison, averaging 85 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for multilingual tasks in this comparison, averaging 79.1 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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