Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiniMax M2.7
53
ZAYA1-74B-Preview
54
Pick ZAYA1-74B-Preview if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
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 ZAYA1-74B-Preview if you want the stronger benchmark profile. MiniMax M2.7 only becomes the better choice if coding is the priority or you would rather avoid the extra latency and token burn of a reasoning model.
ZAYA1-74B-Preview finishes one point ahead on BenchLM's provisional leaderboard, 54 to 53. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
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.
ZAYA1-74B-Preview is ahead on BenchLM's provisional leaderboard, 54 to 53.
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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