Mistral releases Timestral open time-series forecasting models
Confirmed
The takeaway
On Oct 15, 2026 Mistral AI released Timestral, its first family of time-series foundation models, in four sizes (5M, 28M, 123M, 209M parameters) under a modified MIT licence.
Status
- Claim
Confirmed
- Our reporting
- High confidence
- Importance
- 2 of 5
- Last verified
- 15 October 2026
Your AI and this story
- GPT-6 Astra168 days after its cutoff
- Claude Opus 5.5107 days after its cutoff
- Gemini 4 Argon198 days after its cutoff
- Grok 4.7137 days after its cutoff
None of these four assistants can know about it. The closest, Claude Opus 5.5, stops 107 days before it.
Key facts
- Sizes: 5M, 28M, 123M, 209M; Hugging Face repos mistralai/Timestral-5M … mistralai/Timestral-209M; code at github.com/mistralai/timestral; licence: modified MIT
- Context 32,768 time steps; 21-quantile probabilistic forecasts; targets plus past-only and known-future covariates
- Mistral’s claims: 209M ranks first on all five headline metrics of GIFT-Eval (83 entries), TIME (35) and fev-bench (37) among pre-trained zero-shot models; the 28M model beats ~4× larger Chronos-2 on GIFT-Eval and TIME; 209M beats TimesFM-3 and Falcon-2.0 with fewer parameters
- CPU speed (8 threads, one-day forecast from three weeks of 15-minute data): 10 ms (5M) to 140 ms (209M)
- Method: ‘Decoupled Horizon Forecasting’ with learned forecast tokens, covariate-aware attention, mostly synthetic pre-training data; technical report promised later
- Fine-tuning and private deployment via Mistral Forge (on-prem, VPC, air-gapped)
What happened
Mistral entered time-series forecasting, where Mistral benchmarks itself against Chronos-2 and TimesFM-3, with four small open models built for CPUs. The leaderboard claims are Mistral’s own: scores appear only as charts, and the technical report has not been published yet.
Why it matters
Forecasting demand, energy load or sensor data is a large enterprise use of AI that does not need an LLM. Small open models that run on a CPU make it cheap to deploy. The release also widens Mistral’s open-weights lineup beyond language models.
Sources
4 sources from 4 sites. Numbers match the chips in the text.
4 sources: 4 primary
Primary
- Mistral AI: Introducing Timestralmistral.ai, official
- Hugging Face: mistralai/Timestral-209Mhuggingface.co, code
- GitHub: mistralai/timestralgithub.com, code
- Mistral docs: Timestral 1.0docs.mistral.ai, docs
Changes
- Filed (live check of mistral.ai/news)