The lineup includes Nano, Super and Ultra models that leverage a hybrid latent mixture-of-experts (MoE) architecture to deliver higher throughput, extended context reasoning, and scalable performance for multi-agent workflows. Developers and enterprises can access these models, associated data and tooling to build and customize AI agents for tasks ranging from coding and reasoning to complex workflow automation.
Open-Source Models Explained
Open-source AI models are large pretrained neural networks whose weights and code are publicly available for download, inspection, modification and redistribution. By contrast with closed or proprietary models controlled by a single provider, open models enable developers, researchers and enterprises to adapt the model to specific needs, verify behavior, and integrate the technology into their systems without restrictive licensing.
True open-source AI, according to industry definitions, ideally also includes transparency around training data and methodologies to ensure trust and reproducibility. Open-source models matter because they widen access to advanced AI, let independent developers tailor systems to specific domains, and provide transparency that supports safety, auditability and regulatory compliance,
In the broader AI landscape, open models…
