Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 78 to 30. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 (Reasoning)'s sharpest advantage is in mathematics, where it averages 94.4 against 37. The single biggest benchmark swing on the page is HumanEval, 88 to 14.
GLM-5 (Reasoning) 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-5 (Reasoning) gives you the larger context window at 200K, compared with 32K for LFM2.5-1.2B-Instruct.
Pick GLM-5 (Reasoning) 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-5 (Reasoning)
78.3
LFM2.5-1.2B-Instruct
25.7
GLM-5 (Reasoning)
62.5
LFM2.5-1.2B-Instruct
7.2
GLM-5 (Reasoning)
78.5
LFM2.5-1.2B-Instruct
32.4
GLM-5 (Reasoning)
88.9
LFM2.5-1.2B-Instruct
32.1
GLM-5 (Reasoning)
72
LFM2.5-1.2B-Instruct
26
GLM-5 (Reasoning)
92
LFM2.5-1.2B-Instruct
80
GLM-5 (Reasoning)
86.4
LFM2.5-1.2B-Instruct
60.7
GLM-5 (Reasoning)
94.4
LFM2.5-1.2B-Instruct
37
GLM-5 (Reasoning) is ahead overall, 78 to 30. The biggest single separator in this matchup is HumanEval, where the scores are 88 and 14.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 72 versus 26. Inside this category, MMLU is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for coding in this comparison, averaging 62.5 versus 7.2. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for math in this comparison, averaging 94.4 versus 37. Inside this category, AIME 2023 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for reasoning in this comparison, averaging 88.9 versus 32.1. Inside this category, SimpleQA is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for agentic tasks in this comparison, averaging 78.3 versus 25.7. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for multimodal and grounded tasks in this comparison, averaging 78.5 versus 32.4. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for instruction following in this comparison, averaging 92 versus 80. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 86.4 versus 60.7. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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