Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 31B
62
GLM-4.7
67
Pick GLM-4.7 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if knowledge is the priority or you need the larger 256K context window.
Coding
+29.0 difference
Knowledge
+0.7 difference
Gemma 4 31B
GLM-4.7
$0 / $0
$0 / $0
N/A
82 t/s
N/A
1.10s
256K
200K
Pick GLM-4.7 if you want the stronger benchmark profile. Gemma 4 31B only becomes the better choice if knowledge is the priority or you need the larger 256K context window.
GLM-4.7 is clearly ahead on the provisional aggregate, 67 to 62. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in coding, where it averages 70.6 against 41.6. The single biggest benchmark swing on the page is SWE-Rebench, 41.6% to 58.7%. Gemma 4 31B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Gemma 4 31B gives you the larger context window at 256K, compared with 200K for GLM-4.7.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 67 to 62. The biggest single separator in this matchup is SWE-Rebench, where the scores are 41.6% and 58.7%.
Gemma 4 31B has the edge for knowledge tasks in this comparison, averaging 61.3 versus 60.6. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 41.6. Inside this category, SWE-Rebench is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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