Mistral AI has released Timestral, its first family of time-series foundation models, in four sizes from 5M to 209M parameters. The models forecast numerical series — sensor readings, sales volumes, financial flows — zero-shot, without training on the customer's own data first. Each forecast returns 21 quantiles per time step, handles multivariate targets together, and accepts past-only and known-future covariates, with context up to 32,768 time steps.

Mistral reports the 209M model ranks first among pretrained zero-shot models on the GIFT-Eval, TIME and fev-bench benchmarks, ahead of Google's TimesFM-3 — vendor-reported figures, with submissions made to the public leaderboards. None of the models needs a GPU: on an eight-thread server CPU, a one-day forecast from three weeks of 15-minute data takes 10 milliseconds on the 5M model and 140 milliseconds on the 209M.

Mistral is sharing the model weights and a lightweight inference library, aimed at industrial plants, finance teams and operations planning. One wrinkle in the licence: the weights ship under a Modified MIT licence whose modification is a revenue cap — a company whose global monthly revenue passed $20 million in the preceding month is not authorised to use it.