Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiniMax M2.7
62
ZAYA1-74B-Preview
58
Pick MiniMax M2.7 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if you want the cheaper token bill or you need the larger 256K context window.
Coding
+0.5 difference
MiniMax M2.7
ZAYA1-74B-Preview
$0.3 / $1.2
$0 / $0
45 t/s
N/A
2.53s
N/A
200K
256K
Pick MiniMax M2.7 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if you want the cheaper token bill or you need the larger 256K context window.
MiniMax M2.7 is clearly ahead on the provisional aggregate, 62 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M2.7's sharpest advantage is in coding, where it averages 53.7 against 53.2.
MiniMax M2.7 is also the more expensive model on tokens at $0.30 input / $1.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for ZAYA1-74B-Preview. That is roughly Infinityx on output cost alone. ZAYA1-74B-Preview is the reasoning model in the pair, while MiniMax M2.7 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 200K for MiniMax M2.7.
MiniMax M2.7 is ahead on BenchLM's provisional leaderboard, 62 to 58.
MiniMax M2.7 has the edge for coding in this comparison, averaging 53.7 versus 53.2. ZAYA1-74B-Preview stays close enough that the answer can still flip depending on your workload.
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