Tiny Aya: Bridging Scale and Multilingual Depth
Authors: name: Alejandro R. Salamanca, name: Diana Abagyan, name: Daniel D'souza, name: Ammar Khairi, name: David Mora, name: Saurabh Dash, name: Viraat Aryabumi, name: Sara Rajaee, name: Mehrnaz Mofakhami, name: Ananya Sahu, name: Thomas Euyang, name: Brittawnya Prince, name: Madeline Smith, name: Hangyu Lin, name: Acyr Locatelli, name: Sara Hooker, name: Tom Kocmi, name: Aidan Gomez, name: Ivan Zhang, name: Phil Blunsom, name: Nick Frosst, name: Joelle Pineau, name: Beyza Ermis, name: Ahmet Üstün, name: Julia Kreutzer, name: Marzieh Fadaee
Organization: Cohere Labs

Tiny Aya redefines what a small multilingual language model can achieve. Trained on 70 languages and refined through region-aware posttraining, it delivers state-of-the-art in translation quality, strong multilingual understanding, and high-quality target-language generation, all with just 3.35B parameters. The release includes a pretrained foundation model, a globally balanced instruction-tuned variant, and three region-specialized models targeting languages from Africa, South Asia, Europe, Asia-Pacific, and West Asia. This report details the training strategy, data composition, and comprehensive evaluation framework behind Tiny Aya, and presents an alternative scaling path for multilingual AI: one centered on efficiency, balanced performance across languages, and practical deployment.