Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro
70
ZAYA1-8B
62
Verified leaderboard positions: DeepSeek V4 Pro #22 · ZAYA1-8B unranked
Pick DeepSeek V4 Pro if you want the stronger benchmark profile. ZAYA1-8B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+23.7 difference
DeepSeek V4 Pro
ZAYA1-8B
$1.74 / $3.48
$0 / $0
N/A
N/A
N/A
N/A
1M
131K
Pick DeepSeek V4 Pro if you want the stronger benchmark profile. ZAYA1-8B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
DeepSeek V4 Pro is clearly ahead on the provisional aggregate, 70 to 62. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Pro is also the more expensive model on tokens at $1.74 input / $3.48 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for ZAYA1-8B. That is roughly Infinityx on output cost alone. ZAYA1-8B is the reasoning model in the pair, while DeepSeek V4 Pro 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 gives you the larger context window at 1M, compared with 131K for ZAYA1-8B.
DeepSeek V4 Pro is ahead on BenchLM's provisional leaderboard, 70 to 62. The biggest single separator in this matchup is MMLU-Pro, where the scores are 82.9% and 74.2%.
ZAYA1-8B has the edge for knowledge tasks in this comparison, averaging 73.1 versus 49.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
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