Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4
89
Qwen3.6-27B
73
Verified leaderboard positions: GPT-5.4 #16 · Qwen3.6-27B #18
Pick GPT-5.4 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+17.7 difference
Coding
+12.9 difference
Knowledge
+3.9 difference
Multimodal
+3.9 difference
GPT-5.4
Qwen3.6-27B
$2.5 / $15
$0 / $0
74 t/s
N/A
151.79s
N/A
1.05M
262K
Pick GPT-5.4 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5.4 is clearly ahead on the provisional aggregate, 89 to 73. 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 59.3. The single biggest benchmark swing on the page is HLE, 52.1% to 24%. Qwen3.6-27B does hit back in coding, 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 $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. GPT-5.4 gives you the larger context window at 1.05M, compared with 262K for Qwen3.6-27B.
GPT-5.4 is ahead on BenchLM's provisional leaderboard, 89 to 73. The biggest single separator in this matchup is HLE, where the scores are 52.1% and 24%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 66.1 versus 62.2. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for coding in this comparison, averaging 70.6 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 59.3. Inside this category, GDPval-AA is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for multimodal and grounded tasks in this comparison, averaging 76.6 versus 72.7. 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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