Gemini 2.5 Pro vs Qwen3.5-122B-A10B

Treat this as a split decision. Gemini 2.5 Pro makes more sense 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 the better fit if knowledge is the priority or you want the cheaper token bill.

Gemini 2.5 Pro and Qwen3.5-122B-A10B finish on the same provisional overall score, so this is less about a single winner and more about where the edge shows up. The provisional headline says tie; the benchmark table is where the real choice happens.

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