Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 nano
59
Qwen3.5 397B
62
Verified leaderboard positions: GPT-5.4 nano unranked · Qwen3.5 397B #21
Pick Qwen3.5 397B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you want the cheaper token bill or you need the larger 400K context window.
Agentic
+13.3 difference
Knowledge
+12.0 difference
Multimodal
+13.5 difference
GPT-5.4 nano
Qwen3.5 397B
$0.2 / $1.25
$0.6 / $3.6
191 t/s
96 t/s
3.64s
2.44s
400K
128K
Pick Qwen3.5 397B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you want the cheaper token bill or you need the larger 400K context window.
Qwen3.5 397B has the cleaner provisional overall profile here, landing at 62 versus 59. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5 397B's sharpest advantage is in multimodal & grounded, where it averages 79.6 against 66.1. The single biggest benchmark swing on the page is MMMU-Pro, 66.1% to 79%.
Qwen3.5 397B is also the more expensive model on tokens at $0.60 input / $3.60 output per 1M tokens, versus $0.20 input / $1.25 output per 1M tokens for GPT-5.4 nano. That is roughly 2.9x on output cost alone. GPT-5.4 nano 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 nano gives you the larger context window at 400K, compared with 128K for Qwen3.5 397B.
Qwen3.5 397B is ahead on BenchLM's provisional leaderboard, 62 to 59. The biggest single separator in this matchup is MMMU-Pro, where the scores are 66.1% and 79%.
Qwen3.5 397B has the edge for knowledge tasks in this comparison, averaging 65.2 versus 53.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for agentic tasks in this comparison, averaging 56.2 versus 42.9. 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 66.1. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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