Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
GPT-4.1
58
Verified leaderboard positions: GLM-5 #17 · GPT-4.1 unranked
Pick GLM-5 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you need the larger 1M context window.
Coding
+8.6 difference
Knowledge
+4.4 difference
Inst. Following
+5.2 difference
GLM-5
GPT-4.1
$1 / $3.2
$2 / $8
74 t/s
108 t/s
1.64s
1.02s
200K
1M
Pick GLM-5 if you want the stronger benchmark profile. GPT-4.1 only becomes the better choice if you need the larger 1M context window.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 58. 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 54.6. The single biggest benchmark swing on the page is SWE-bench Verified, 77.8% to 54.6%.
GPT-4.1 is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $1.00 input / $3.20 output per 1M tokens for GLM-5. That is roughly 2.5x on output cost alone. GPT-4.1 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 58. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 77.8% and 54.6%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 66.3. 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 54.6. 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 87.4. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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