Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
74
GPT-5.4 nano
59
Verified leaderboard positions: GLM-5.1 #30 · GPT-5.4 nano unranked
Pick GLM-5.1 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+22.4 difference
Knowledge
+0.9 difference
GLM-5.1
GPT-5.4 nano
$1.4 / $4.4
$0.2 / $1.25
N/A
191 t/s
N/A
3.64s
203K
400K
Pick GLM-5.1 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GLM-5.1 is clearly ahead on the provisional aggregate, 74 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in agentic, where it averages 65.3 against 42.9. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 63.5% to 46.3%. GPT-5.4 nano does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 is also the more expensive model on tokens at $1.40 input / $4.40 output per 1M tokens, versus $0.20 input / $1.25 output per 1M tokens for GPT-5.4 nano. That is roughly 3.5x on output cost alone. GPT-5.4 nano gives you the larger context window at 400K, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 74 to 59. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 63.5% and 46.3%.
GPT-5.4 nano has the edge for knowledge tasks in this comparison, averaging 53.2 versus 52.3. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for agentic tasks in this comparison, averaging 65.3 versus 42.9. Inside this category, Tau2-Telecom is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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