Head-to-head comparison across 4benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4
93
Qwen3.6-27B
72
Verified leaderboard positions: GPT-5.4 #4 · Qwen3.6-27B #10
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
+30.6 difference
Multimodal
+5.4 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, 93 to 72. 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 knowledge, where it averages 92.8 against 62.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 75.1% to 59.3%. 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, 93 to 72. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 75.1% and 59.3%.
GPT-5.4 has the edge for knowledge tasks in this comparison, averaging 92.8 versus 62.2. Inside this category, GPQA 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, SWE-bench Pro 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, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.4 has the edge for multimodal and grounded tasks in this comparison, averaging 81.2 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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