The v0 model architecture combines 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 to achieve significantly higher quality in code generation. This composite model allows for quick upgrades to the latest frontier model while keeping the rest of the architecture stable, enabling v0 to tackle complex tasks like building full-stack web applications with ease. The model's pre-processing step retrieves additional context based on user queries from a dataset, and its state-of-the-art base models handle generation tasks, with smaller edits routed to an optimized Quick Edit model for speed. A custom AutoFix model is used to catch and fix errors mid-stream, improving output quality. v0 models substantially outperform their base model counterparts in error-free code generation, and the development team plans to continue improving model output and releasing new model classes in the upcoming months.