Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
o3-mini
55
ZAYA1-74B-Preview
54
Pick o3-mini if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if coding is the priority or you want the cheaper token bill.
Coding
+3.9 difference
Knowledge
+12.9 difference
o3-mini
ZAYA1-74B-Preview
$1.1 / $4.4
$0 / $0
160 t/s
N/A
7.12s
N/A
200K
256K
Pick o3-mini if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if coding is the priority or you want the cheaper token bill.
o3-mini finishes one point ahead on BenchLM's provisional leaderboard, 55 to 54. 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.
o3-mini's sharpest advantage is in knowledge, where it averages 77.2 against 64.3. The single biggest benchmark swing on the page is GPQA, 77.2% to 57.3%. ZAYA1-74B-Preview does hit back in coding, so the answer changes if that is the part of the workload you care about most.
o3-mini is also the more expensive model on tokens at $1.10 input / $4.40 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 gives you the larger context window at 256K, compared with 200K for o3-mini.
o3-mini is ahead on BenchLM's provisional leaderboard, 55 to 54. The biggest single separator in this matchup is GPQA, where the scores are 77.2% and 57.3%.
o3-mini has the edge for knowledge tasks in this comparison, averaging 77.2 versus 64.3. 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 49.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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