Multi-model use inside generative AI services is already common, with different-sized models dividing work, a Ravlub researcher said. Small models handle routine tasks such as search, information gathering and summarisation, while frontier models take key decisions and generate outputs. As agents spread, repeatedly calling top-performance models can push inference costs higher. He said small models will not replace frontier models, citing scaling, but role-based use of multiple model sizes will expand.

AI coding startup Poolside said it has unveiled an open-weight foundation model for coding agents called Laguna S 2.1. The company said the model has 118 billion parameters and showed performance that matched or exceeded several larger models on Terminal-Bench 2.1 and SWE-Bench Pro. It said the model can run on a single Nvidia DGX Spark system and that its weights are available on Hugging Face under the OpenMDW-1.1 license.