aiqbench analysis
This guide details a cost-optimization strategy for deploying LLMs by using self-reported confidence scores to route uncertain queries to stronger models while keeping confident ones on cheaper instances. The core technical insight is that these scores should be treated as relative rankings rather than calibrated probabilities, requiring continuous tuning against specific traffic error rates to maintain accuracy without incurring unnecessary inference costs.
Confidence Thresholds for Model Escalation Routing
Confidence-based escalation keeps most requests on a cheap model and sends only the ones it is unsure about to a stronger one. This guide covers forcing a numeric confidence field with structured outputs, setting the threshold from error rates on your own traffic, tuning it against accuracy, cost, and latency, routing the escalation in your code, and re-tuning it after launch.
Sending every request to your strongest model gets good answers at the highest price, because you pay frontier rates for requests a cheaper model would have answered correctly. Fixed routing rules, such as picking the model by keyword or task type, cost less but need rewriting as your traffic changes. Confidence-based escalation sits between the two. You ask the model to score its own answer, then route on that score. Answers with a high score stay on a cheap model. Answers with a low score go to a stronger one. This guide sets that up in five steps. Confidence-based escalation routes each request on a score the model reports for its own answer, so a cheap model handles the requests it is sure about and a stronger model only sees the ones it is not. Force the score with structured outputs. A JSON schema that requires a numeric confidence field gives you the same signal from every model, instead of guessing from hedge words in free text. Use the ranking, not the absolute number. A 0.85 is not a calibrated 85 percent chance of being correct. Check on your own traffic that lower-scored answers are wrong more often than higher-scored…
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