Research · LLM

Distilling an LLM into a task model 1000× cheaper

Replace a large-LLM API call on a high-volume task (classification, routing, tagging) with a small model trained on the LLM’s own judgements.

up to 1000×lower cost per prediction
100×+faster: milliseconds, not seconds
CPUruns without GPUs

Pipeline

  1. A large LLM labels a representative sample using a written rubric
  2. Experts review disagreements; the rubric is refined
  3. A compact encoder model (e.g. ModernBERT / DeBERTa) is trained on the labels
  4. Confidence thresholds route uncertain cases back to the LLM
  5. Continuous evaluation against fresh LLM and human labels

Figures show the typical order of magnitude for this approach compared with calling a large general-purpose model. Actual results depend on the task and data; we measure them on your data during the baseline phase.

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