Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 Pro
92
Qwen3.6-27B
72
Verified leaderboard positions: GPT-5.4 Pro unranked · Qwen3.6-27B #10
Pick GPT-5.4 Pro if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Agentic
+30.0 difference
Knowledge
+13.2 difference
Multimodal
+18.2 difference
GPT-5.4 Pro
Qwen3.6-27B
$30 / $180
$0 / $0
74 t/s
N/A
151.79s
N/A
1.05M
262K
Pick GPT-5.4 Pro if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-5.4 Pro is clearly ahead on the provisional aggregate, 92 to 72. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 Pro's sharpest advantage is in agentic, where it averages 89.3 against 59.3. The single biggest benchmark swing on the page is HLE, 58.7% to 24%. Qwen3.6-27B does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GPT-5.4 Pro is also the more expensive model on tokens at $30.00 input / $180.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. GPT-5.4 Pro gives you the larger context window at 1.05M, compared with 262K for Qwen3.6-27B.
GPT-5.4 Pro is ahead on BenchLM's provisional leaderboard, 92 to 72. The biggest single separator in this matchup is HLE, where the scores are 58.7% and 24%.
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 62.2 versus 49. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.4 Pro has the edge for agentic tasks in this comparison, averaging 89.3 versus 59.3. Qwen3.6-27B stays close enough that the answer can still flip depending on your workload.
GPT-5.4 Pro has the edge for multimodal and grounded tasks in this comparison, averaging 94 versus 75.8. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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