Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Mellum2-12B-A2.5B-Instruct
27
Qwen3.6-27B
73
Verified leaderboard positions: Mellum2-12B-A2.5B-Instruct unranked · Qwen3.6-27B #19
Pick Qwen3.6-27B if you want the stronger benchmark profile. Mellum2-12B-A2.5B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+33.4 difference
Knowledge
+21.3 difference
Mellum2-12B-A2.5B-Instruct
Qwen3.6-27B
N/A
$0 / $0
N/A
N/A
N/A
N/A
128K
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. Mellum2-12B-A2.5B-Instruct only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 73 to 27. 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 37.2. The single biggest benchmark swing on the page is GPQA, 40.9% to 87.8%.
Qwen3.6-27B is the reasoning model in the pair, while Mellum2-12B-A2.5B-Instruct is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.6-27B gives you the larger context window at 262K, compared with 128K for Mellum2-12B-A2.5B-Instruct.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 73 to 27. The biggest single separator in this matchup is GPQA, where the scores are 40.9% and 87.8%.
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 62.2 versus 40.9. 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 37.2. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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