The 70/30 Model Selection Rule: Stop Using GPT-4 for Everything

Most AI agents use one model for everything. That's like using a sledgehammer for both nails and screws. Here's the reality: 70% of your agent's inference calls don't need a frontier model. The Problem I see this pattern constantly: # Every call goes to GPT-4 response = openai . chat . completions . create ( model = " gpt-4-turbo " , messages = [{ " role " : " user " , " content " : " Classify this email as spam or not spam " }] ) GPT-4 Turbo costs ~$10/1M input tokens. For email classification, you're paying 100x what you need to. The 70/30 Split After analyzing thousands of agent inference calls across different workloads, a clear pattern emerges: 70% of calls are "commodity" tasks: Classification (spam/not spam, category assignment) Extraction (pull name/date/amount from text) Summarization (condense to key points) Embeddings (vector representations) Format conversion (JSON ↔ text) These tasks are deterministic. A 7B parameter model handles them at 95%+ accuracy. 30% of calls are "f