Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 E2B
23
GLM-5
67
Verified leaderboard positions: Gemma 4 E2B unranked · GLM-5 #19
Pick GLM-5 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Knowledge
+16.6 difference
Gemma 4 E2B
GLM-5
$0 / $0
$1 / $3.2
N/A
74 t/s
N/A
1.64s
128K
200K
Pick GLM-5 if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 23. 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 knowledge, where it averages 70.7 against 54.1. The single biggest benchmark swing on the page is GPQA, 43.4% to 86%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E2B. That is roughly Infinityx on output cost alone. Gemma 4 E2B is the reasoning model in the pair, while GLM-5 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 gives you the larger context window at 200K, compared with 128K for Gemma 4 E2B.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 23. The biggest single separator in this matchup is GPQA, where the scores are 43.4% and 86%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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