6 points | by george_ciobanu 14 hours ago ago
2 comments
The prompt went from 44k to 6k tokens, but you're making two extra model calls per round to get there (chunker + working_memory_update). What does the all-in cost comparison actually look like?
the proxy uses a cheap, small model (like gpt-5.4-mini by default) behind the scenes to save tokens on the expensive main model.
Because the proxy has a little bit of overhead per turn, the break-even point depends entirely on session length.
Short sessions (e.g., 2 rounds): The proxy's overhead might actually cost you more than you save.
Long sessions (e.g., 69 to 190 rounds): The token savings on the main model are massive and completely dwarf the small model's overhead.
It's not a universal win for quick, one-off queries, but the math becomes highly favorable on long, complex debugging sessions.
The prompt went from 44k to 6k tokens, but you're making two extra model calls per round to get there (chunker + working_memory_update). What does the all-in cost comparison actually look like?
the proxy uses a cheap, small model (like gpt-5.4-mini by default) behind the scenes to save tokens on the expensive main model.
Because the proxy has a little bit of overhead per turn, the break-even point depends entirely on session length.
Short sessions (e.g., 2 rounds): The proxy's overhead might actually cost you more than you save.
Long sessions (e.g., 69 to 190 rounds): The token savings on the main model are massive and completely dwarf the small model's overhead.
It's not a universal win for quick, one-off queries, but the math becomes highly favorable on long, complex debugging sessions.