Smaller models are used for simple tasks (model routing by complexity)

Frontier models like GPT-4o cost 10–50x more per token than their lightweight counterparts — and are often no better at simple tasks. Generating a document title, classifying sentiment, or summarizing a short paragraph does not require frontier-level reasoning. Every request where gpt-4o runs a classification that gpt-4o-mini handles equally well is a 17x cost premium with zero quality benefit. NIST AI RMF MAP 5.1 requires that model selection be matched to operational requirements; using the most powerful model for every task uniformly fails this requirement.

Define a TASK_MODELS map that explicitly assigns each task type to an appropriate model tier. Import from it at every call site instead of hardcoding a single model string.

// src/lib/ai/models.ts
export const TASK_MODELS = {
  titleGeneration: "gpt-4o-mini",
  classification: "gpt-4o-mini",
  summarization: "gpt-4o-mini",
  sentimentAnalysis: "gpt-4o-mini",
  codeGeneration: "gpt-4o",
  complexReasoning: "gpt-4o",
  multiStepPlanning: "gpt-4o",
} as const;

// src/app/api/title/route.ts
import { TASK_MODELS } from "@/lib/ai/models";

const { text } = await generateText({
  model: openai(TASK_MODELS.titleGeneration),
  prompt: `Generate a concise title for: ${content}`,
  maxTokens: 50,
});

Verify by reviewing each API route's model parameter and confirming that simple classification, labeling, or summarization tasks are not using frontier models.