Mistral's 209M-parameter Timestral tops all three time-series forecasting benchmarks
AxSaucedo · x · 2026-10-20
Mistral has released Timestral, its first family of time-series foundation models. The largest version, at just 209M parameters, ranks first on all three major public benchmarks — GIFT-Eval, TIME, and fev-bench — beating the previous state of the art with only two-thirds the parameters.
Timestral delivers probabilistic, multivariate forecasts and supports both past-only and known-future covariates. The family spans 5M to 209M parameters, sits on the Pareto front of accuracy vs. model size, runs efficiently on CPUs, and excels at zero-shot forecasting across domains. It combines synthetic pre-training, covariate-aware attention, and long-context learning, with domain fine-tuning available via Mistral Forge. The poster also highlights t0-gamma from The Forecasting Company, the other European model to top forecasting leaderboards the same week.
More from Models
- LightOn open-sources SOTA sparse retrievers SlideOn at 174M and 339M params — antoine_chaffin · 2026-10-20
- Codex quota keeps getting reset: users say gpt-6.1-sol barely dents limits, want it to stay — bytebot · 2026-10-20
- Fudan & Shanghai AI Lab map PEFT recipes for MoE LLMs: tune experts alone, LoRA/DoRA win — kalyan_kpl · 2026-10-20
- Reddit User: New Models Have Mostly Stopped Hallucinating — Have LLMs Crossed the Reliability Line? — LiveNotWork · 2026-10-20
- 26-30B is the best dense model size range, argues ML commentator — auto_grad_ · 2026-10-20
- Researchers push back on Opus 4/4.1 deprecation, citing alignment and model welfare concerns — Sauers_ · 2026-10-20