We use the smaller models for everything that’s not internal high complexity tasks like coding. Although they would do a good enough of a job there as well, we happily pay the uncharge to get something a little better here.
Anything user facing or when building workflow functionalities like extracting, converting, translating, merging, evaluating, all of these are mini and nano cases at our company.
I have a number of agents in ~/.claude/agents/. Currently have most set to `model: sonnet` but some are on haiku.
The agents are given very specific instructions and names that define what they do, like `feature-implementation-planner` and `feature-implementer`. My (naive) approach is to use higher-cost models to plan and ideally hand off to a sub-agent that uses a lower-cost model to implement, then use a higher-cost to code review.
I am either not noticing the handoffs, or they are not happening unless specifically instructed. I even have a `claude-help` agent, and I asked it how to pipe/delegate tasks to subagents as you're describing, and it answered that it ought to detect it automatically. I tested it and asked it to report if any such handoffs were detected and made, and it failed on both counts, even having that initial question in its context!
The rules themselves are a bit more complex and require a smarter model, but the arbitration should be fairly fast. GPT-5 is cheap and high quality but even gpt-5-mini takes about 20-40 seconds to handle a scene. Sonnet can hit 8 seconds with RAG but it's too expensive for freemium.
Grok Turbo and Haiku 3 were fast but often misses the mark. I'm hoping Haiku 4.5 can go below 4 seconds and have decent accuracy. 20 seconds is too long, and hurts debugging as well.