Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 26B A4B
55
GLM-5
67
Verified leaderboard positions: Gemma 4 26B A4B unranked · GLM-5 #19
Pick GLM-5 if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if you want the cheaper token bill or you need the larger 256K context window.
Knowledge
+21.5 difference
Gemma 4 26B A4B
GLM-5
$0 / $0
$1 / $3.2
N/A
74 t/s
N/A
1.64s
256K
200K
Pick GLM-5 if you want the stronger benchmark profile. Gemma 4 26B A4B only becomes the better choice if you want the cheaper token bill or you need the larger 256K context window.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 55. 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 49.2. The single biggest benchmark swing on the page is HLE, 17.2% to 50.4%.
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 26B A4B. That is roughly Infinityx on output cost alone. Gemma 4 26B A4B 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. Gemma 4 26B A4B gives you the larger context window at 256K, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 55. The biggest single separator in this matchup is HLE, where the scores are 17.2% and 50.4%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 49.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
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