Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 2.5 Pro
67
GLM-5
77
Verified leaderboard positions: Gemini 2.5 Pro unranked · GLM-5 #12
Pick GLM-5 if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if coding is the priority or you need the larger 1M context window.
Coding
+0.6 difference
Knowledge
+29.9 difference
Gemini 2.5 Pro
GLM-5
$1.25 / $5
$0 / $0
117 t/s
74 t/s
21.19s
1.64s
1M
200K
Pick GLM-5 if you want the stronger benchmark profile. Gemini 2.5 Pro only becomes the better choice if coding is the priority or you need the larger 1M context window.
GLM-5 is clearly ahead on the provisional aggregate, 77 to 67. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in knowledge, where it averages 70.7 against 40.8. The single biggest benchmark swing on the page is HLE, 18.8% to 50.4%. Gemini 2.5 Pro does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $5.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-5. That is roughly Infinityx on output cost alone. Gemini 2.5 Pro gives you the larger context window at 1M, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 77 to 67. The biggest single separator in this matchup is HLE, where the scores are 18.8% and 50.4%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 40.8. Inside this category, HLE is the benchmark that creates the most daylight between them.
Gemini 2.5 Pro has the edge for coding in this comparison, averaging 63.8 versus 63.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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