DeepSeek V3 vs Laguna M.1

Pick Laguna M.1 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.

Laguna M.1 is clearly ahead on the provisional aggregate, 46 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Laguna M.1's sharpest advantage is in coding, where it averages 56.4 against 39.2. The single biggest benchmark swing on the page is SWE-bench Verified, 42% to 72.5%.

DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 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 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.