Skeptical of 'small model for evals' moats: labs will distill it into cheaper models
Shahules786 · x · 2026-10-08
A developer pushes back on monitoring platforms pitching a dedicated small model for evaluation and error analysis as a differentiator. He tried this in early 2024: post-training a small model for a specific workload does work and is far cheaper than a frontier model.
The catch: labs can distill those capabilities into cheaper general models (e.g., Astra → Luna), and they have the data, compute, and internal demand to keep improving them. The incentive is especially strong because researchers need evals throughout training and data prep—so every lab has reason to build these capabilities into its own models.
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