Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
68
o3-mini
55
Pick GLM-4.7 if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority.
Coding
+21.3 difference
Knowledge
+16.6 difference
GLM-4.7
o3-mini
$0 / $0
$1.1 / $4.4
82 t/s
160 t/s
1.10s
7.12s
200K
200K
Pick GLM-4.7 if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority.
GLM-4.7 is clearly ahead on the provisional aggregate, 68 to 55. 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 49.3. The single biggest benchmark swing on the page is SWE-bench Verified, 73.8% to 49.3%. o3-mini does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 68 to 55. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.8% and 49.3%.
o3-mini has the edge for knowledge tasks in this comparison, averaging 77.2 versus 60.6. Inside this category, AA-HLE 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 49.3. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
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