# Open-source model optimization tool: Low cost, high quality Improve your specialized models and cut costs in half. - **Source:** [Hacker News: Front Page](https://brifing.si/source/b07aa772-b1a0-4a76-aa18-719c6f9e2dde) - **Published:** 2026-07-26 - **Topic:** [Data & AI](https://brifing.si/c/data-ai) - **Original article:** - **On Brifing:** https://brifing.si/s/31ca5616-1c0a-42de-8e02-8e05b422b23b ## Introduction to model optimization World-model-optimizer is an open-source tool designed to continuously improve your specialized models. With `wmo serve`, you can route repetitive tasks to smaller, optimized models. This allows you to reduce costs while maintaining quality. ## Distillation method Enhance your specialized models through distillation from open-source models. Continuous improvement ensures your models become more efficient over time. This method reduces the complexity of large models while preserving performance. ## Model routing The router decides which tasks should go to frontier models and which to specialized models. This intelligent routing helps you use resources most efficiently. Token compaction removes unnecessary data to further reduce costs. ## Local and hosted solutions Set up a local OpenAI-compatible endpoint to run your own models. Alternatively, use hosted solutions to save up to 40% in costs. Both options offer continuous improvement over time. ## Demo and access Watch our demo video to see how the tool works. Start using it immediately by entering your OpenRouter key and traces. You can also join the waitlist to access the hosted solution. ## Why it matters This tool allows you to run specialized models at a lower cost. Continuous improvement ensures your models become more efficient over time. This is a significant advantage, especially for teams with limited resources. ## Sources covering this story - https://github.com/experientiallabs/world-model-optimizer — --- _Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._