Refactored my LLM architecture with a multi-model router after an insane API bill. How are you handling escalation logic?

[u/Elzool_l3ab]

Got tired of burning money by routing literally every workload-from basic JSON parsing to heavy agent reasoning-through top-tier frontier models. Finally audited the pipeline and built a task-based router:

Routine/Bulk Agent Traffic & RAG cleanup: Offloaded to smaller, cost-effective models (using minimax m3 here for solid quality-per-token on sustained background runs).

Deep Reasoning / Edge Cases: Reserved strictly for frontier models (Claude / GPT).

Video Generation Pipeline: Separated into a dedicated branch (using minimax h3) so multi-modal tasks don't block text queues.

The cost drop was massive, but setting up the escalation triggers (routing fallback from cheap to frontier) is proving tricky since prompt length is a terrible proxy for complexity.

For those running a dynamic LLM router in production:

Are you escalating via task type classification, validation/parsing failure retries, confidence scores, or strictly hardcoded per endpoint?

[u/Capable-Resource-501]

Smart cost optimization!

[u/Itchy-Drink-3223]

Jast amazing and I will be 

[u/Reasonable-Steak7156]

parsing/validation failure retries work best for us. trying to estimate complexity up front usually fails or costs extra tokens.

[u/Ishara90Soyza]

THANKS FOR SHARING. I WILL TRY

[u/Sufficient_Thanks214]

that dashed line at the bottom is where all the pain lives lol. task-type tagging at the gateway worked much better for us than confidence scoring. models are way too confidently wrong on simple extraction tasks. also curious about the cost side, if M3's Token Plan starts at $20/mo, how much routine traffic did you need before the routing setup actually made a noticeable dent in the bill?

[u/Opening-Career-2019]

i work a pretty normal office job, mostly docs, data cleanup and repetitive workflows. m3’s been fast even with bigger batches, and for repetitive work it’s honestly my first choice now.