DeepSeek V3.2 vs Laguna M.1

Pick DeepSeek V3.2 if you want the stronger benchmark profile. Laguna M.1 only becomes the better choice if you want the cheaper token bill or you need the larger 131K context window.

DeepSeek V3.2 is clearly ahead on the provisional aggregate, 58 to 46. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

DeepSeek V3.2's sharpest advantage is in coding, where it averages 60.9 against 56.4.

DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Laguna M.1. That is roughly Infinityx on output cost alone. Laguna M.1 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. Laguna M.1 gives you the larger context window at 131K, compared with 128K for DeepSeek V3.2.