You’re Overpaying for Intelligence: The Case for Small, Specialized Models

Question

Most of your LLM bill pays a frontier model to do jobs a small model handles fine.

Why now:
This week’s releases skew small and specialized: Claude Haiku 5.5, JetBrains Mellum2.1, Google’s EmbeddingGemma 2, Perplexity’s embedding models, and Cloudflare’s Clef and Clef-Flash. Vendors are clearly investing in the cheap tier.

Key points:

Which tasks suit small models: classification, extraction, embeddings and routing, autocomplete, and summarization of short text.
Which tasks still need a big model: ambiguous multi-step reasoning, long-horizon agent work, and anything with a high cost of error.
Routing pattern: try the small model first and escalate on low confidence. Measure the escalation rate.
Cost and latency come with it, plus an option for on-device or edge deployment.

Call to action:
What are your top five most frequent LLM calls and whether each one needs a frontier model.