DeepSeek V3.2 vs Mistral Medium 3.5 128B

Pick Mistral Medium 3.5 128B 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.

Mistral Medium 3.5 128B is clearly ahead on the provisional aggregate, 78 to 56. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Mistral Medium 3.5 128B's sharpest advantage is in coding, where it averages 77.6 against 60.9.

Mistral Medium 3.5 128B is also the more expensive model on tokens at $1.50 input / $7.50 output per 1M tokens, versus $0.28 input / $0.42 output per 1M tokens for DeepSeek V3.2. That is roughly 17.9x on output cost alone. Mistral Medium 3.5 128B 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. Mistral Medium 3.5 128B gives you the larger context window at 256K, compared with 128K for DeepSeek V3.2.