Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
GPT-5.2
83
Pick GPT-5.2 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+9.9 difference
Coding
+5.9 difference
Knowledge
+31.8 difference
GLM-4.7
GPT-5.2
$0 / $0
$2 / $8
82 t/s
73 t/s
1.10s
130.34s
200K
400K
Pick GPT-5.2 if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5.2 is clearly ahead on the provisional aggregate, 83 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.2's sharpest advantage is in knowledge, where it averages 92.4 against 60.6. The single biggest benchmark swing on the page is BrowseComp, 52% to 65.8%. GLM-4.7 does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.2 is also the more expensive model on tokens at $2.00 input / $8.00 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. GPT-5.2 gives you the larger context window at 400K, compared with 200K for GLM-4.7.
GPT-5.2 is ahead on BenchLM's provisional leaderboard, 83 to 71. The biggest single separator in this matchup is BrowseComp, where the scores are 52% and 65.8%.
GPT-5.2 has the edge for knowledge tasks in this comparison, averaging 92.4 versus 60.6. Inside this category, GPQA 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 64.7. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.2 has the edge for agentic tasks in this comparison, averaging 55.2 versus 45.3. Inside this category, BrowseComp is the benchmark that creates the most daylight between them.
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