Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-4.7-Flash is clearly ahead on the aggregate, 64 to 39. 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 multilingual, where it averages 85 against 80.6. The single biggest benchmark swing on the page is HumanEval, 58 to 82.6. Phi-4 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-4.7-Flash is the reasoning model in the pair, while Phi-4 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 16K for Phi-4.
Pick GLM-4.7-Flash if you want the stronger benchmark profile. Phi-4 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
GLM-4.7-Flash
57.3
Phi-4
70.5
GLM-4.7-Flash
47.7
Phi-4
82.6
GLM-4.7-Flash
85
Phi-4
80.6
GLM-4.7-Flash is ahead overall, 64 to 39. The biggest single separator in this matchup is HumanEval, where the scores are 58 and 82.6.
Phi-4 has the edge for knowledge tasks in this comparison, averaging 70.5 versus 57.3. Inside this category, MMLU is the benchmark that creates the most daylight between them.
Phi-4 has the edge for coding in this comparison, averaging 82.6 versus 47.7. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-4.7-Flash has the edge for multilingual tasks in this comparison, averaging 85 versus 80.6. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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