Mistral presents Large 4 as a 1-trillion-parameter natively multimodal model, with 52 billion active parameters, now available in public preview.
Why it matters: For organizations doing security work, open weights and self-deployment could reduce dependence on provider-level refusals or access during an incident. But the weights are still planned, and the reported comparisons should be read with the source’s caveat about differing refusal policies.
EmbeddingGemma 2 extends on-device embeddings beyond text, mapping combinations of text, code, images, video, and audio into a shared embedding space.
Why it matters: Local embeddings can support cross-modal search and retrieval entirely offline, helping protect data privacy and reduce pipeline latency. The RAM figures offer a device-specific reference for planning deployment, not a universal memory guarantee.