Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
68
GPT-5.4 mini
68
Treat this as a split decision. GLM-4.7 makes more sense if knowledge is the priority or you want the cheaper token bill; GPT-5.4 mini is the better fit if agentic is the priority or you need the larger 400K context window.
Agentic
+20.3 difference
Knowledge
+3.2 difference
GLM-4.7
GPT-5.4 mini
$0 / $0
$0.75 / $4.5
82 t/s
201 t/s
1.10s
3.85s
200K
400K
Treat this as a split decision. GLM-4.7 makes more sense if knowledge is the priority or you want the cheaper token bill; GPT-5.4 mini is the better fit if agentic is the priority or you need the larger 400K context window.
GLM-4.7 and GPT-5.4 mini finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.
GPT-5.4 mini is also the more expensive model on tokens at $0.75 input / $4.50 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 mini gives you the larger context window at 400K, compared with 200K for GLM-4.7.
GLM-4.7 and GPT-5.4 mini are tied on the provisional overall score, so the right pick depends on which category matters most for your use case.
GLM-4.7 has the edge for knowledge tasks in this comparison, averaging 60.6 versus 57.4. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.4 mini has the edge for agentic tasks in this comparison, averaging 65.6 versus 45.3. Inside this category, GDPval-AA 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.