Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GLM-4.7-Flash is clearly ahead on the aggregate, 62 to 38. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7-Flash's sharpest advantage is in agentic, where it averages 61.3 against 33.4. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 64 to 30.
GLM-4.7-Flash is the reasoning model in the pair, while LFM2-24B-A2B 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-Flash gives you the larger context window at 200K, compared with 32K for LFM2-24B-A2B.
Pick GLM-4.7-Flash if you want the stronger benchmark profile. LFM2-24B-A2B only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7-Flash
61.3
LFM2-24B-A2B
33.4
GLM-4.7-Flash
45.9
LFM2-24B-A2B
18
GLM-4.7-Flash
62.5
LFM2-24B-A2B
41.7
GLM-4.7-Flash
69.7
LFM2-24B-A2B
46.6
GLM-4.7-Flash
54.1
LFM2-24B-A2B
35.6
GLM-4.7-Flash
84
LFM2-24B-A2B
68
GLM-4.7-Flash
81.8
LFM2-24B-A2B
61.4
GLM-4.7-Flash
74
LFM2-24B-A2B
50.4
GLM-4.7-Flash is ahead overall, 62 to 38. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 64 and 30.
GLM-4.7-Flash has the edge for knowledge tasks in this comparison, averaging 54.1 versus 35.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for coding in this comparison, averaging 45.9 versus 18. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for math in this comparison, averaging 74 versus 50.4. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for reasoning in this comparison, averaging 69.7 versus 46.6. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for agentic tasks in this comparison, averaging 61.3 versus 33.4. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for multimodal and grounded tasks in this comparison, averaging 62.5 versus 41.7. Inside this category, OfficeQA Pro is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for instruction following in this comparison, averaging 84 versus 68. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for multilingual tasks in this comparison, averaging 81.8 versus 61.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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