Meta trains 8B Qwen3 model to beat larger LLMs at tool selection
Small models trained specifically for tool selection can outperform much larger prompted LLMs on the routing step, so agent builders can cut cost and latency by replacing the proprietary LLM in their tool-selection pipeline. The selector's ability to work with different retrievers and unseen domains makes it practical to slot into existing RAG-style stacks. Teams building large-scale agent tooling should compare this approach before defaulting to a frontier model for routing.
