Combining many AI models VS. SOTA models like Grok 3 or Claude 3.7

I've been deep diving into most of the latest SOTA models - Claude 3.7, O3 mini-high, Grok 3, Deepseek, etc. They're great, and I love the Deepsearch/Deep research use cases.

But here's my controversial take: The REAL revolution still isn't happening with individual models, but in how apps like @Tana are orchestrating multiple AI models together for specific workflows.

In Tana, I can:

• Get meeting notes automatically, and they come out perfectly transcribed. Tasks, random insights, and agenda items don’t just sit there—they get pulled out and sorted into my knowledge graph without me lifting a finger.

• Go for a walk-and-talk, spill all my half-baked ideas, questions, and to-dos, and by the time I’m back, everything’s already shuffled off to the right task boards, pages, or tables.

• Throw in some articles or research papers, and smarter models churn out summaries while the lighter, cheaper ones handle the grunt work—grabbing metadata like dates, authors, topics, whatever, and slotting it where it belongs.

Things are moving crazy fast, no doubt, but this multi-model setup still feels like a 10x boost over any single AI assistant, even the bleeding-edge ones.

Anyone else finding that their most valuable AI use cases come from these "model cocktails" rather than raw access to the latest flagship models? Or am I missing something about how you're using these advanced models directly?

And if you are using @Tana , what's your experience with Tana's AI implementation specifically? And what other apps are nailing this "AI orchestration" approach?