Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemini 2.5 Pro
65
ZAYA1-8B
62
Pick Gemini 2.5 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
+32.3 difference
Gemini 2.5 Pro
ZAYA1-8B
$1.25 / $10
$0 / $0
117 t/s
N/A
21.19s
N/A
1M
131K
Pick Gemini 2.5 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.
Gemini 2.5 Pro has the cleaner provisional overall profile here, landing at 65 versus 62. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $10.00 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 Gemini 2.5 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. Gemini 2.5 Pro gives you the larger context window at 1M, compared with 131K for ZAYA1-8B.
Gemini 2.5 Pro is ahead on BenchLM's provisional leaderboard, 65 to 62. The biggest single separator in this matchup is GPQA, where the scores are 83% and 71%.
ZAYA1-8B has the edge for knowledge tasks in this comparison, averaging 73.1 versus 40.8. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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