Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiniMax M3
76
ZAYA1-74B-Preview
54
Verified leaderboard positions: MiniMax M3 #12 · ZAYA1-74B-Preview unranked
Pick MiniMax M3 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Coding
+13.8 difference
MiniMax M3
ZAYA1-74B-Preview
$0.3 / $1.2
$0 / $0
N/A
N/A
N/A
N/A
1M
256K
Pick MiniMax M3 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
MiniMax M3 is clearly ahead on the provisional aggregate, 76 to 54. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiniMax M3's sharpest advantage is in coding, where it averages 67 against 53.2. The single biggest benchmark swing on the page is SWE-bench Verified, 80.5% to 53.2%.
MiniMax M3 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 M3 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. MiniMax M3 gives you the larger context window at 1M, compared with 256K for ZAYA1-74B-Preview.
MiniMax M3 is ahead on BenchLM's provisional leaderboard, 76 to 54. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 80.5% and 53.2%.
MiniMax M3 has the edge for coding in this comparison, averaging 67 versus 53.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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