Gemini 2.5 Pro vs Kimi K2.6

Pick Kimi K2.6 if you want the stronger benchmark profile. Gemini 2.5 Pro 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.

Kimi K2.6 is clearly ahead on the provisional aggregate, 84 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Kimi K2.6's sharpest advantage is in knowledge, where it averages 53.8 against 40.8. The single biggest benchmark swing on the page is SWE-bench Verified, 63.8% to 80.2%.

Gemini 2.5 Pro is also the more expensive model on tokens at $1.25 input / $10.00 output per 1M tokens, versus $0.95 input / $4.00 output per 1M tokens for Kimi K2.6. That is roughly 2.5x on output cost alone. Kimi K2.6 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 256K for Kimi K2.6.