# World-Model-Optimizer: Cutting LLM Costs via Distillation > Source: > Published: 2026-07-27 00:02:21+00:00 # World-Model-Optimizer: Cutting LLM Costs via Distillation `wmo serve` tool hits exactly that pain point by routing tasks to smaller, distilled models.The core logic is simple: it uses your existing agent traces to identify patterns. Instead of hitting a frontier model every time, it distills that knowledge into a smaller model and routes the repetitive stuff there. Here is how the workflow actually functions: 1. **Trace Capture:** It takes the logs/traces your agents are already generating. 2. **Distillation:** It uses those traces to refine a specialized smaller model. 3. **Routing:** A router decides in real-time if a request needs the "big" model or if the custom distilled model can handle it. 4. **Compaction:** It strips out noise to reduce token usage. For a local setup, you just provide the traces and an OpenRouter key. It spins up an OpenAI-compatible endpoint locally. As more traces come in, the system keeps training, meaning the smaller model gets more accurate over time. From a deployment perspective, this is a practical way to build a production AI workflow where you aren't paying "frontier prices" for basic tasks. The claim is a 40%+ reduction in costs while maintaining equivalent quality. If you want to check out the demo, the link is here: ``` https://www.youtube.com/watch?v=2_m4Ze6mdko ``` For those who don't want to manage the infrastructure, there is a hosted version available via their waitlist: ``` https://experientiallabs.ai ``` [Next Saying "no" to a feature or a deploy is actually a high-value →](/en/threads/3886/)