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 33. 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 mathematics, where it averages 85 against 42.3. The single biggest benchmark swing on the page is HumanEval, 78 to 17.
GLM-4.7 gives you the larger context window at 200K, compared with 32K for LFM2.5-1.2B-Thinking.
Pick GLM-4.7 if you want the stronger benchmark profile. LFM2.5-1.2B-Thinking only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-4.7
66.1
LFM2.5-1.2B-Thinking
34.1
GLM-4.7
46.6
LFM2.5-1.2B-Thinking
8.2
GLM-4.7
70.5
LFM2.5-1.2B-Thinking
32.4
GLM-4.7
80.2
LFM2.5-1.2B-Thinking
38.4
GLM-4.7
61.8
LFM2.5-1.2B-Thinking
27
GLM-4.7
85
LFM2.5-1.2B-Thinking
72
GLM-4.7
79.1
LFM2.5-1.2B-Thinking
60.7
GLM-4.7
85
LFM2.5-1.2B-Thinking
42.3
GLM-4.7 is ahead overall, 67 to 33. The biggest single separator in this matchup is HumanEval, where the scores are 78 and 17.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 61.8 versus 27. 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 8.2. 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 42.3. 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 38.4. 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 34.1. Inside this category, BrowseComp 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 72. 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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