Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 is clearly ahead on the aggregate, 65 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in mathematics, where it averages 84.8 against 37. The single biggest benchmark swing on the page is HumanEval, 80 to 14.
GLM-5 gives you the larger context window at 200K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick GLM-5 if you want the stronger benchmark profile. LFM2.5-1.2B-Instruct only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GLM-5
62.3
LFM2.5-1.2B-Instruct
25.7
GLM-5
41.1
LFM2.5-1.2B-Instruct
7.2
GLM-5
69.2
LFM2.5-1.2B-Instruct
32.4
GLM-5
79.4
LFM2.5-1.2B-Instruct
32.1
GLM-5
62.1
LFM2.5-1.2B-Instruct
26
GLM-5
85
LFM2.5-1.2B-Instruct
80
GLM-5
82.1
LFM2.5-1.2B-Instruct
60.7
GLM-5
84.8
LFM2.5-1.2B-Instruct
37
GLM-5 is ahead overall, 65 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 80 and 14.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 62.1 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-5 has the edge for coding in this comparison, averaging 41.1 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-5 has the edge for math in this comparison, averaging 84.8 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-5 has the edge for reasoning in this comparison, averaging 79.4 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-5 has the edge for agentic tasks in this comparison, averaging 62.3 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-5 has the edge for multimodal and grounded tasks in this comparison, averaging 69.2 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GLM-5 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-5 has the edge for multilingual tasks in this comparison, averaging 82.1 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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