According to DeepLearning.AI on X, Mistral launched the open-weights Ministral 3 family (14B, 8B, 3B) compressed from a larger model via a new pruning and distillation method called cascade distillation; the vision-language variants rival or outperform similarly sized models, indicating higher parameter efficiency and lower inference costs (as reported by DeepLearning.AI). According to Mistral’s announcement referenced by DeepLearning.AI, the cascade distillation pipeline prunes and transfers knowledge in stages, enabling compact checkpoints that preserve multimodal reasoning quality, which can reduce GPU memory footprint and latency for on-device and edge deployments. As reported by DeepLearning.AI, open weights allow enterprises to self-host, fine-tune on proprietary data, and control data residency, creating opportunities for cost-optimized VLM applications in e-commerce visual search, industrial inspection, and mobile assistants. According to DeepLearning.AI, the family span (3B–14B) lets builders match model size to throughput needs, supporting batch inference on consumer GPUs and enabling A/B testing across model scales for price-performance tuning.