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One challenge/opportunity I've had is harnessing really wide running cheap agents. Any thoughts on how to move really cheap agents beyond basic summarization so we can go broader than the pricing of frontier llms allows?

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Your “really cheap” agents can’t be so cheap that they do not have good tool calling skills. But! Using bigger models to put guardrails in place as static verifiers allows lower complexity changes to “self steer” as tests fail, which means coming down on the cost curve is more effective.

I was experimenting and found deepseek-v4-flash and found it cheap (way cheap compared to sota models) and perfectly good at tool calling. I did a post on it https://martincapodici.com/2026/07/18/weekly-ai-learnings-3/

So $1.5 for 40m tokens I guess would cost much more with sota (but would need less tokens perhaps).

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