DeepSeek V4 Flash (High) vs Kimi K2.5

Pick DeepSeek V4 Flash (High) if you want the stronger benchmark profile. Kimi K2.5 only becomes the better choice if knowledge is the priority or you would rather avoid the extra latency and token burn of a reasoning model.

DeepSeek V4 Flash (High) is clearly ahead on the provisional aggregate, 71 to 64. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

DeepSeek V4 Flash (High)'s sharpest advantage is in coding, where it averages 72.2 against 64.2. The single biggest benchmark swing on the page is BrowseComp, 53.5% to 60.6%. Kimi K2.5 does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.

Kimi K2.5 is also the more expensive model on tokens at $0.60 input / $3.00 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (High). That is roughly 10.7x on output cost alone. DeepSeek V4 Flash (High) is the reasoning model in the pair, while Kimi K2.5 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. DeepSeek V4 Flash (High) gives you the larger context window at 1M, compared with 256K for Kimi K2.5.