Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
68
GPT-5.4 nano
59
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Agentic
+2.4 difference
Knowledge
+7.4 difference
GLM-4.7
GPT-5.4 nano
$0 / $0
$0.2 / $1.25
82 t/s
191 t/s
1.10s
3.64s
200K
400K
Pick GLM-4.7 if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
GLM-4.7 is clearly ahead on the provisional aggregate, 68 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-4.7's sharpest advantage is in knowledge, where it averages 60.6 against 53.2. The single biggest benchmark swing on the page is HLE, 24.8% to 37.7%.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GPT-5.4 nano gives you the larger context window at 400K, compared with 200K for GLM-4.7.
GLM-4.7 is ahead on BenchLM's provisional leaderboard, 68 to 59. The biggest single separator in this matchup is HLE, where the scores are 24.8% and 37.7%.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 53.2. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
GLM-4.7 has the edge for agentic tasks in this comparison, averaging 45.3 versus 42.9. Inside this category, Tau2-Telecom is the benchmark that creates the most daylight between them.
For engineers, researchers, and the plain curious — a weekly brief on new models, ranking shifts, and pricing changes.
Free. No spam. Unsubscribe anytime.