DeepSeek V3.2 vs Kimi K2.6

Pick Kimi K2.6 if you want the stronger benchmark profile. DeepSeek V3.2 only becomes the better choice if you want the cheaper token bill 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 58. 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 coding, where it averages 72 against 60.9.

Kimi K2.6 is also the more expensive model on tokens at $0.95 input / $4.00 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 9.5x on output cost alone. Kimi K2.6 is the reasoning model in the pair, while DeepSeek V3.2 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. Kimi K2.6 gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.