We recently launched our AI models v0 -1.5-md , v0 -1.5-lg , and v0 -1.0-md in v0 . Today, we're sharing a deep dive into the composite model architecture behind those models. They combine specialized knowledge from retrieval-augmented generation (RAG), reasoning from state-of-the-art large language models (LLMs), and error fixing from a custom streaming post-processing model. While this may sound complex, it enables v0 to achieve significantly higher quality when generating code. Further, as base models improve, we can quickly upgrade to the latest frontier model while keeping the rest of the architecture stable. Link to heading Why does v0 need a composite model architecture? Most AI models¹ are one of two things: a proprietary model from a frontier model provider or an open-source model from a third-party host.…