Mistral AI released Mistral Large 4 (Le Chonk) as a public preview with native image input and an API that is live now, while model weights are scheduled to be delivered later. The preview lists training and compute details including training from scratch on 3,800 NVIDIA Grace Blackwell GPUs at Mistral’s EU datacenters. With the API available immediately but weights not yet ship-ready for self-hosting, developers can start using the model now under the published per-token pricing, with deployment options changing when the weights arrive by the end of October 2026.
Mistral is launching a public preview of Mistral Large 4 (ML4), including a try-it option via the preview API on Mistral Studio. The model is described as a 1 trillion-parameter natively multimodal system with 49 billion active parameters, with weights scheduled for release by the end of this month. The preview expands access to an open-weights, multimodal frontier model while the company red-teams it with partners and authorities and will publish more architecture and benchmark details before the weight drop.
Google DeepMind launched EmbeddingGemma 2, an open embedding model that maps text, code, images, video, and audio into one shared representation for cross-modal search. The full multimodal model is 740M parameters and needs about 567MB of RAM on a Pixel 11 Pro. Model weights are now available on Apache 2.0 via Hugging Face and Kaggle, and the MTEB Code score rose from 68.76 to 78.68.
Reka released a research preview of Rho-1, a 19B omni-reasoning model that handles text, images, video, and robot actions within one system. Rho-1’s base model produced a first video clip in about 7.0 seconds, versus 13.8 seconds for an illustrative multi-agent pipeline. Distilled Rho-1 reduced denoising from 99 to 8 steps and generated a 5.3-second clip in about 1 second.
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