Head-to-head comparison across 5benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.5
89
Qwen3.5 397B
66
Verified leaderboard positions: GPT-5.5 #2 · Qwen3.5 397B #11
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
+25.6 difference
Coding
+1.7 difference
Reasoning
+21.8 difference
Knowledge
+1.2 difference
Multimodal
+10.0 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, 89 to 66. 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 81.8 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 82.7% 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, 89 to 66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 82.7% and 52.5%.
GPT-5.5 has the edge for knowledge tasks in this comparison, averaging 66.4 versus 65.2. Inside this category, HLE 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, SWE-bench Pro 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. Qwen3.5 397B stays close enough that the answer can still flip depending on your workload.
GPT-5.5 has the edge for agentic tasks in this comparison, averaging 81.8 versus 56.2. Inside this category, Terminal-Bench 2.0 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 versus 69. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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