Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
o1
57
Qwen3.5-27B
63
Verified leaderboard positions: o1 unranked · Qwen3.5-27B #16
Pick Qwen3.5-27B if you want the stronger benchmark profile. o1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Knowledge
+4.9 difference
Inst. Following
+2.8 difference
o1
Qwen3.5-27B
$15 / $60
$0 / $0
98 t/s
N/A
32.29s
N/A
200K
262K
Pick Qwen3.5-27B if you want the stronger benchmark profile. o1 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Qwen3.5-27B is clearly ahead on the provisional aggregate, 63 to 57. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 80.6 against 75.7. The single biggest benchmark swing on the page is GPQA, 75.7% to 85.5%.
o1 is also the more expensive model on tokens at $15.00 input / $60.00 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. Qwen3.5-27B gives you the larger context window at 262K, compared with 200K for o1.
Qwen3.5-27B is ahead on BenchLM's provisional leaderboard, 63 to 57. The biggest single separator in this matchup is GPQA, where the scores are 75.7% and 85.5%.
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 80.6 versus 75.7. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Qwen3.5-27B has the edge for instruction following in this comparison, averaging 95 versus 92.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
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