DeepSeek V4 Flash vs ZAYA1-8B

Pick ZAYA1-8B if you want the stronger benchmark profile. DeepSeek V4 Flash 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.

ZAYA1-8B has the cleaner provisional overall profile here, landing at 62 versus 59. It is a real lead, but still close enough that category-level strengths matter more than the headline number.

ZAYA1-8B's sharpest advantage is in knowledge, where it averages 73.1 against 45.2. The single biggest benchmark swing on the page is MMLU-Pro, 83% to 74.2%.

DeepSeek V4 Flash is also the more expensive model on tokens at $0.14 input / $0.28 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 Flash 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 Flash gives you the larger context window at 1M, compared with 131K for ZAYA1-8B.