It really depends on which models you are comparing. These days, parameter count and architecture really don't do a great job of predicting the model's capabilities.

The reigning champion for a fast, high-intelligence LLM right now is Qwen 3.8 Flash Next at 125B + 50B of embeddings (can be offloaded to SSD for ~free), 6B active parameters. It's somewhere in the Opus 4.6-4.8 range, noticeably stronger than 3.8 27B, despite having only 6B active. Compare Meta's Muse Spark Open, which has 816B total and 42B active, and does not even reach the performance of Flash Next.