GPT-4.1 nano vs Qwen3.5-122B-A10B

Pick Qwen3.5-122B-A10B 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.

Qwen3.5-122B-A10B is clearly ahead on the provisional aggregate, 65 to 27. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.5-122B-A10B's sharpest advantage is in knowledge, where it averages 81.6 against 50.3. The single biggest benchmark swing on the page is GPQA, 50.3% to 86.6%.

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 Qwen3.5-122B-A10B. That is roughly Infinityx on output cost alone. Qwen3.5-122B-A10B 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 262K for Qwen3.5-122B-A10B.