Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 mini
73
Qwen3.6-27B
72
Verified leaderboard positions: GPT-5.4 mini unranked · Qwen3.6-27B #10
Pick GPT-5.4 mini 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
+6.3 difference
Knowledge
+4.8 difference
Multimodal
+0.8 difference
GPT-5.4 mini
Qwen3.6-27B
$0.75 / $4.5
$0 / $0
201 t/s
N/A
3.85s
N/A
400K
262K
Pick GPT-5.4 mini 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 mini finishes one point ahead on BenchLM's provisional leaderboard, 73 to 72. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
GPT-5.4 mini's sharpest advantage is in agentic, where it averages 65.6 against 59.3. The single biggest benchmark swing on the page is HLE, 41.5% 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 mini is also the more expensive model on tokens at $0.75 input / $4.50 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 mini gives you the larger context window at 400K, compared with 262K for Qwen3.6-27B.
GPT-5.4 mini is ahead on BenchLM's provisional leaderboard, 73 to 72. The biggest single separator in this matchup is HLE, where the scores are 41.5% and 24%.
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 62.2 versus 57.4. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.4 mini has the edge for agentic tasks in this comparison, averaging 65.6 versus 59.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.4 mini has the edge for multimodal and grounded tasks in this comparison, averaging 76.6 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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