Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
64
GLM-5.2
94
Verified leaderboard positions: Gemma 4 31B unranked · GLM-5.2 #9
Pick GLM-5.2 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if you want the cheaper token bill.
Coding
+20.5 difference
Knowledge
+5.9 difference
Gemma 4 31B
GLM-5.2
$0 / $0
$1.4 / $4.4
N/A
N/A
N/A
N/A
256K
1M
Pick GLM-5.2 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if you want the cheaper token bill.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2's sharpest advantage is in coding, where it averages 62.1 against 41.6. The single biggest benchmark swing on the page is HLE, 26.5% to 54.7%.
GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 31B. That is roughly Infinityx on output cost alone. GLM-5.2 gives you the larger context window at 1M, compared with 256K for Gemma 4 31B.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 64. The biggest single separator in this matchup is HLE, where the scores are 26.5% and 54.7%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 61.3. Inside this category, HLE is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 41.6. Gemma 4 31B stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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