Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 nano
59
Qwen3.5-27B
61
Verified leaderboard positions: GPT-5.4 nano unranked · Qwen3.5-27B #23
Pick Qwen3.5-27B 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
+8.7 difference
Knowledge
+27.4 difference
GPT-5.4 nano
Qwen3.5-27B
$0.2 / $1.25
$0 / $0
191 t/s
N/A
3.64s
N/A
400K
262K
Pick Qwen3.5-27B if you want the stronger benchmark profile. GPT-5.4 nano only becomes the better choice if you need the larger 400K context window.
Qwen3.5-27B has the cleaner provisional overall profile here, landing at 61 versus 59. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 80.6 against 53.2. The single biggest benchmark swing on the page is OSWorld-Verified, 39% to 56.2%.
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 Qwen3.5-27B. That is roughly Infinityx on output cost alone. GPT-5.4 nano gives you the larger context window at 400K, compared with 262K for Qwen3.5-27B.
Qwen3.5-27B is ahead on BenchLM's provisional leaderboard, 61 to 59. The biggest single separator in this matchup is OSWorld-Verified, where the scores are 39% and 56.2%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 53.2. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for agentic tasks in this comparison, averaging 51.6 versus 42.9. Inside this category, Tau2-Telecom is the benchmark that creates the most daylight between them.
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