Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4
89
Qwen3.5 397B
63
Verified leaderboard positions: GPT-5.4 #16 · Qwen3.5 397B #19
Pick GPT-5.4 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
Agentic
+20.8 difference
Coding
+2.6 difference
Knowledge
+0.9 difference
Multimodal
+6.9 difference
GPT-5.4
Qwen3.5 397B
$2.5 / $15
$0.6 / $3.6
74 t/s
96 t/s
151.79s
2.44s
1.05M
128K
Pick GPT-5.4 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if multimodal & grounded is the priority or you want the cheaper token bill.
GPT-5.4 is clearly ahead on the provisional aggregate, 89 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4's sharpest advantage is in agentic, where it averages 77 against 56.2. The single biggest benchmark swing on the page is HLE, 52.1% to 28.7%. Qwen3.5 397B does hit back in multimodal & grounded, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 is also the more expensive model on tokens at $2.50 input / $15.00 output per 1M tokens, versus $0.60 input / $3.60 output per 1M tokens for Qwen3.5 397B. That is roughly 4.2x on output cost alone. GPT-5.4 is the reasoning model in the pair, while Qwen3.5 397B is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.4 gives you the larger context window at 1.05M, compared with 128K for Qwen3.5 397B.
GPT-5.4 is ahead on BenchLM's provisional leaderboard, 89 to 63. The biggest single separator in this matchup is HLE, where the scores are 52.1% and 28.7%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 66.1 versus 65.2. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for coding in this comparison, averaging 60.3 versus 57.7. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for agentic tasks in this comparison, averaging 77 versus 56.2. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79.6 versus 72.7. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
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