Gemma 4 E2B vs GPT-4.1 nano

Pick Gemma 4 E2B if you want the stronger benchmark profile. GPT-4.1 nano 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.

Gemma 4 E2B finishes one point ahead on BenchLM's provisional leaderboard, 32 to 31. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.

Gemma 4 E2B's sharpest advantage is in knowledge, where it averages 54.1 against 50.3. The single biggest benchmark swing on the page is GPQA, 43.4% to 50.3%.

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 Gemma 4 E2B. That is roughly Infinityx on output cost alone. Gemma 4 E2B 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 128K for Gemma 4 E2B.