GPT-4.1 nano vs ZAYA1-8B

Pick ZAYA1-8B if you want the stronger benchmark profile. GPT-4.1 nano only becomes the better choice if instruction following is the priority or you need the larger 1M context window.

ZAYA1-8B is clearly ahead on the provisional aggregate, 62 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

ZAYA1-8B's sharpest advantage is in knowledge, where it averages 73.1 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 71%. GPT-4.1 nano does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.

GPT-4.1 nano is also the more expensive model on tokens at $0.10 input / $0.40 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 GPT-4.1 nano 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. GPT-4.1 nano gives you the larger context window at 1M, compared with 131K for ZAYA1-8B.