AI model selection strategy

By @edhonour · Instagram

Source: https://www.instagram.com/reel/DdTvhbwphjz/?stkn=eThicTQzbnVndGxm

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Summary

Picking the right AI model for a task matters more than always grabbing the strongest one, since token costs and latency add up fast. The speaker lays out two evaluation strategies: start with the best model and downgrade, or start with the cheapest model and upgrade until performance is reliable.

Core ideas

One context card plus 7 ideas

Bigger isn't always better: The strongest model often produces the best answers but comes with the highest latency, which hurts real world performance.
Right model, not best model: The goal is matching model capability to task difficulty rather than defaulting to the top tier every time.
Cost and speed matter: As token usage scales, efficiency and reliability become part of the actual engineering problem, not an afterthought.
Top down approach: Starting with the strongest model confirms the task is solvable before you try cheaper options, which the speaker prefers.
Bottom up approach: Starting with lightweight models like 4.1 mini or Haiku and upgrading only when needed keeps costs low from the start.
Emotional tradeoff: The bottom up method risks early failures that can shake confidence the system will ever work, even if it's more cost efficient.
Consistency over raw power: A model only counts as "good enough" if it performs reliably across repeated runs, not just once.

Quotes

“You need to use the right model for the right task.”
@edhonour

“You don't necessarily need to use the best model or the biggest model every time.”
@edhonour

“I get a little scared that it's ever going to work.”
@edhonour

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