> There isn't, pretty much everyone wants the best of the best.

For direct user interaction or coding problems, perhaps. But as API calls get cheaper, it becomes more realistic to use them for completely automated workflows against data-sets, or as sub-agents called from expensive SOTA models.

For example, in Claude, using Opus as an orchestrator to call Sonnet sub-agents, is a popular usage "hack." That only gets more powerful, as the Sonnet equivalent model gets cheaper. Now you can spawn entire teams of small specialized sub-agents with small context windows but limited scope.

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alexsmirnov 7 hours ago | parent | next [-]

Exactly.

I did create my own MCP with custom agents that combine several tools into a single one. For example, all WebSearch, WebFetch, Context7 exposed as a single "web research" tool, backed by the cheapest model that passes evaluation. The same for a codebase research

Use it with both Claude and Opencode saves a lot of time and tokens.

	▲	hadlock 2 hours ago \| parent [-]
		I'd be interested in seeing the source for this if you have a moment

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thinkcontext 7 hours ago | parent | prev | next [-]

> But as API calls get cheaper, it becomes more realistic to use them for completely automated workflows against data-sets

Seems like a huge waste of money and electricity for processes that can be implemented as a traditional deterministic program. One would hope that tools would identify recurrent jobs that can be turned into simple scripts.

	▲	Someone1234 6 hours ago \| parent [-]
		It depends on the specific task. For example: "Here our dataset that contains customer feedback comment fields; look through them, draw out themes, associations, and look for trends." Solving that with a deterministic program isn't a trivial problem, and it is likely cheaper solved via LLM.

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jimbokun 5 hours ago | parent | prev [-]

That is a very complex, high level use case that takes time to configure and orchestrate.

There are many simpler tasks that would work fine with a simpler, local model.