World-Model-Optimizer: Cutting LLM Costs via Distillation

World-Model-Optimizer, a tool from Experiential Labs, cuts LLM costs by over 40% through distillation and routing, using agent traces to refine smaller models for repetitive tasks while reserving frontier models for complex requests.

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/