Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
GPT-5.5 Pro
100
Verified leaderboard positions: GLM-5 #14 · GPT-5.5 Pro unranked
Pick GPT-5.5 Pro if you want the stronger benchmark profile. GLM-5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+33.9 difference
Knowledge
+13.5 difference
GLM-5
GPT-5.5 Pro
$1 / $3.2
$30 / $180
74 t/s
N/A
1.64s
N/A
200K
1M
Pick GPT-5.5 Pro if you want the stronger benchmark profile. GLM-5 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-5.5 Pro is clearly ahead on the provisional aggregate, 100 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5 Pro's sharpest advantage is in agentic, where it averages 90.1 against 56.2. The single biggest benchmark swing on the page is HLE, 50.4% to 57.2%. GLM-5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-5.5 Pro is also the more expensive model on tokens at $30.00 input / $180.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 56.3x on output cost alone. GPT-5.5 Pro 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. GPT-5.5 Pro gives you the larger context window at 1M, compared with 200K for GLM-5.
GPT-5.5 Pro is ahead on BenchLM's provisional leaderboard, 100 to 67. The biggest single separator in this matchup is HLE, where the scores are 50.4% and 57.2%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 57.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.5 Pro has the edge for agentic tasks in this comparison, averaging 90.1 versus 56.2. GLM-5 stays close enough that the answer can still flip depending on your workload.
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