Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro Base
43
ZAYA1-74B-Preview
58
Pick ZAYA1-74B-Preview if you want the stronger benchmark profile. DeepSeek V4 Pro Base only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+0.9 difference
DeepSeek V4 Pro Base
ZAYA1-74B-Preview
$null / $null
$0 / $0
N/A
N/A
N/A
N/A
1M
256K
Pick ZAYA1-74B-Preview if you want the stronger benchmark profile. DeepSeek V4 Pro Base only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.
ZAYA1-74B-Preview is clearly ahead on the provisional aggregate, 58 to 43. 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 63.4. The single biggest benchmark swing on the page is MMLU-Pro, 73.5% to 68.1%.
ZAYA1-74B-Preview is the reasoning model in the pair, while DeepSeek V4 Pro Base 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. DeepSeek V4 Pro Base gives you the larger context window at 1M, compared with 256K for ZAYA1-74B-Preview.
ZAYA1-74B-Preview is ahead on BenchLM's provisional leaderboard, 58 to 43. The biggest single separator in this matchup is MMLU-Pro, where the scores are 73.5% and 68.1%.
ZAYA1-74B-Preview has the edge for knowledge tasks in this comparison, averaging 64.3 versus 63.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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