Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
GPT-4.1 nano
26
Verified leaderboard positions: GLM-5 #23 · GPT-4.1 nano unranked
Pick GLM-5 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Knowledge
+20.4 difference
Inst. Following
+9.4 difference
GLM-5
GPT-4.1 nano
$1 / $3.2
$0.1 / $0.4
74 t/s
181 t/s
1.64s
0.63s
200K
1M
Pick GLM-5 if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 26. 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 50.3. The single biggest benchmark swing on the page is GPQA, 86% to 50.3%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.10 input / $0.40 output per 1M tokens for GPT-4.1 nano. That is roughly 8.0x on output cost alone. GPT-4.1 nano gives you the larger context window at 1M, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 26. The biggest single separator in this matchup is GPQA, where the scores are 86% and 50.3%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 50.3. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 83.2. Inside this category, AA-IFBench is the benchmark that creates the most daylight between them.
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