Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
GPT-4.1 mini
46
Verified leaderboard positions: GLM-5 #17 · GPT-4.1 mini unranked
Pick GLM-5 if you want the stronger benchmark profile. GPT-4.1 mini only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Coding
+39.6 difference
Knowledge
+6.5 difference
Inst. Following
+4.1 difference
GLM-5
GPT-4.1 mini
$1 / $3.2
$0.4 / $1.6
74 t/s
80 t/s
1.64s
0.76s
200K
1M
Pick GLM-5 if you want the stronger benchmark profile. GPT-4.1 mini 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 46. 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 coding, where it averages 63.2 against 23.6. The single biggest benchmark swing on the page is SWE-bench Verified, 77.8% to 23.6%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.40 input / $1.60 output per 1M tokens for GPT-4.1 mini. That is roughly 2.0x on output cost alone. GPT-4.1 mini 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 46. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 77.8% and 23.6%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 64.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5 has the edge for coding in this comparison, averaging 63.2 versus 23.6. Inside this category, SWE-bench Verified 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 88.5. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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