Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 nano
62
Qwen3.6-27B
72
Verified leaderboard positions: GPT-5.4 nano unranked · Qwen3.6-27B #10
Pick Qwen3.6-27B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Agentic
+16.4 difference
Knowledge
+9.0 difference
Multimodal
+9.7 difference
GPT-5.4 nano
Qwen3.6-27B
$0.2 / $1.25
$0 / $0
191 t/s
N/A
3.64s
N/A
400K
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 72 to 62. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-27B's sharpest advantage is in agentic, where it averages 59.3 against 42.9. The single biggest benchmark swing on the page is HLE, 37.7% to 24%.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 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 nano gives you the larger context window at 400K, compared with 262K for Qwen3.6-27B.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 72 to 62. The biggest single separator in this matchup is HLE, where the scores are 37.7% and 24%.
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 62.2 versus 53.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.6-27B has the edge for agentic tasks in this comparison, averaging 59.3 versus 42.9. Inside this category, Terminal-Bench 2.0 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 75.8 versus 66.1. 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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