Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
o3-mini
58
Qwen3.6-27B
72
Verified leaderboard positions: o3-mini unranked · Qwen3.6-27B #10
Pick Qwen3.6-27B if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority.
Coding
+21.3 difference
Knowledge
+15.0 difference
o3-mini
Qwen3.6-27B
$1.1 / $4.4
$0 / $0
160 t/s
N/A
7.12s
N/A
200K
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. o3-mini only becomes the better choice if knowledge is the priority.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 72 to 58. 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 coding, where it averages 70.6 against 49.3. The single biggest benchmark swing on the page is SWE-bench Verified, 49.3% to 77.2%. o3-mini does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 200K for o3-mini.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 72 to 58. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49.3% and 77.2%.
o3-mini has the edge for knowledge tasks in this comparison, averaging 77.2 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 49.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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