Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Mellum2-12B-A2.5B-Instruct
27
ZAYA1-74B-Preview
54
Pick ZAYA1-74B-Preview 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
+16.0 difference
Knowledge
+23.4 difference
Mellum2-12B-A2.5B-Instruct
ZAYA1-74B-Preview
N/A
$0 / $0
N/A
N/A
N/A
N/A
128K
256K
Pick ZAYA1-74B-Preview 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.
ZAYA1-74B-Preview is clearly ahead on the provisional aggregate, 54 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
ZAYA1-74B-Preview's sharpest advantage is in knowledge, where it averages 64.3 against 40.9. The single biggest benchmark swing on the page is GPQA, 40.9% to 57.3%.
ZAYA1-74B-Preview 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. ZAYA1-74B-Preview gives you the larger context window at 256K, compared with 128K for Mellum2-12B-A2.5B-Instruct.
ZAYA1-74B-Preview is ahead on BenchLM's provisional leaderboard, 54 to 27. The biggest single separator in this matchup is GPQA, where the scores are 40.9% and 57.3%.
ZAYA1-74B-Preview has the edge for knowledge tasks in this comparison, averaging 64.3 versus 40.9. Inside this category, GPQA is the benchmark that creates the most daylight between them.
ZAYA1-74B-Preview has the edge for coding in this comparison, averaging 53.2 versus 37.2. Mellum2-12B-A2.5B-Instruct stays close enough that the answer can still flip depending on your workload.
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