Head-to-head comparison across 5benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.5
91
Qwen3.5 397B
63
Verified leaderboard positions: GPT-5.5 #5 · Qwen3.5 397B #19
Pick GPT-5.5 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
+25.3 difference
Coding
+1.7 difference
Reasoning
+21.8 difference
Knowledge
+1.2 difference
Multimodal
+9.2 difference
GPT-5.5
Qwen3.5 397B
$5 / $30
$0.6 / $3.6
N/A
96 t/s
N/A
2.44s
1M
128K
Pick GPT-5.5 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-5.5 is clearly ahead on the provisional aggregate, 91 to 63. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.5's sharpest advantage is in agentic, where it averages 81.5 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 82% to 52.5%. 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-5.5 is also the more expensive model on tokens at $5.00 input / $30.00 output per 1M tokens, versus $0.60 input / $3.60 output per 1M tokens for Qwen3.5 397B. That is roughly 8.3x on output cost alone. GPT-5.5 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-5.5 gives you the larger context window at 1M, compared with 128K for Qwen3.5 397B.
GPT-5.5 is ahead on BenchLM's provisional leaderboard, 91 to 63. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 82% and 52.5%.
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 66.4 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 58.6. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
GPT-5.5 has the edge for reasoning in this comparison, averaging 85 versus 63.2. Inside this category, CritPt is the benchmark that creates the most daylight between them.
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.5 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 70.4. Inside this category, AA-MMMU-Pro is the benchmark that creates the most daylight between them.
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