> ## Documentation Index > Fetch the complete documentation index at: https://docs.scaledown.xyz/llms.txt > Use this file to discover all available pages before exploring further. # Pareto merging # Overview #### Created as part of Eleuther SOAR by Nadya Devani, Pouya Sadeghi, Purva Kandalgaonkar, Suparnojit Sarkar The **Pareto Merging method** is an advanced technique for combining multiple specialized AI models into a single, highly efficient model. Unlike traditional methods that create one static merged model, Pareto Merging produces a flexible framework that can generate an infinite number of model variations tailored to specific needs without retraining. This method merges two types of models: a concise, non-reasoning Large Language Model (LLM) and a powerful Large Reasoning Model (LRM). The key innovation is a two-part architecture: 1. **Preference-Independent Base Model**: A single, fixed base model combines the general knowledge of the LLM with the reasoning capabilities of the LRM. 2. **Preference-Dependent Tensor**: A small, trainable "control knob" that is adjusted on-the-fly based on a predicted **question difficulty score**. This allows the system to be dynamic: for easy questions, it leans towards the concise base model to save costs, and for difficult questions, it engages more reasoning power to ensure high accuracy. ### Architecture pareto ## Pareto Merging API ### Overview The ScaleDown Pareto Merging API provides dynamic, cost-effective reasoning. It merges a concise LLM with a powerful LRM, allowing you to balance response accuracy and token cost on a per-query basis. Instead of using a static model, our system adjusts its reasoning depth based on the predicted difficulty of your prompt, reducing costs by up to 30% while maintaining high accuracy. *** ### Quick Start Get started in minutes by using our hosted API endpoint or by running the model locally with our Python package. #### Installation ```bash theme={null} # Placeholder for package installation pip install scaledown-paretomerge ```