An open-source framework to achieve Gemini 3 Deep Think / GPT-5.2 Pro level performance with local models scaffolding

A viral open-source project is demonstrating how to achieve performance rivaling top-tier models like Google's Gemini 3 Deep Think and OpenAI's GPT-5.2 Pro by using a clever 'scaffolding' architecture with local, smaller models. Instead of relying on a single massive model, the framework orchestrates a team of specialized open-source models (e.g., Llama 3 70B, Mixtral 8x22B) to handle different parts of a complex reasoning task. One model might act as a planner to break down a problem, another conducts research via retrieval-augmented generation (RAG), and a third synthesizes and refines the final answer. This agentic approach, shared on platforms like Reddit, effectively creates a system where the whole is greater than the sum of its parts. The key innovation is the orchestration layer that manages context and handoffs between these specialized agents, enabling deep, multi-step reasoning previously only seen in models with trillions of parameters. For developers and companies, this represents a significant shift. It offers a path to state-of-the-art AI capabilities that are more customizable, private, and cost-effective than constantly calling expensive API endpoints from OpenAI or Google. While it requires more technical setup and hardware (like powerful GPUs for local inference), it democratizes access to cutting-edge AI reasoning. This trend towards 'composite AI systems' is likely to accelerate as open-source models continue to improve, challenging the dominance of monolithic, proprietary models from big tech companies.