Side-by-side benchmark comparison across knowledge, coding, math, and reasoning.
GLM-5 (Reasoning) is clearly ahead on the aggregate, 84 to 39. 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 knowledge, where it averages 80.3 against 70.5. The single biggest benchmark swing on the page is GPQA, 94 to 56.1. Phi-4 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-5 (Reasoning) 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-5 (Reasoning) gives you the larger context window at 200K, compared with 16K for Phi-4.
Pick GLM-5 (Reasoning) 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-5 (Reasoning)
80.3
Phi-4
70.5
GLM-5 (Reasoning)
69.3
Phi-4
82.6
GLM-5 (Reasoning)
89
Phi-4
80.6
GLM-5 (Reasoning) is ahead overall, 84 to 39. The biggest single separator in this matchup is GPQA, where the scores are 94 and 56.1.
GLM-5 (Reasoning) has the edge for knowledge tasks in this comparison, averaging 80.3 versus 70.5. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Phi-4 has the edge for coding in this comparison, averaging 82.6 versus 69.3. Inside this category, HumanEval is the benchmark that creates the most daylight between them.
GLM-5 (Reasoning) has the edge for multilingual tasks in this comparison, averaging 89 versus 80.6. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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