Is it cheaper to use smaller models for simple tasks?
Why smaller models cost less
API pricing is usually based on input and output tokens. Smaller models have fewer parameters, so providers charge less per million tokens. For example, a small model might cost $0.10 per million input tokens, while a large model costs $3.00 or more.
For simple tasks, the quality difference is often small. If you need to classify a support ticket or extract a date from text, a small model can do it accurately. The savings add up quickly at scale.
- Small models: ~$0.10–$0.50 per million input tokens
- Large models: ~$3–$15 per million input tokens
- Use small models for: classification, extraction, simple Q&A
- Use large models for: complex reasoning, creative writing, multi-step tasks
When small models might not be enough
If the task requires deep reasoning, nuanced understanding, or long context, a small model may fail or need many retries. Retries increase token usage and can erase savings.
Test both models on a sample of your real data. Measure accuracy and cost per correct answer, not just cost per token. Sometimes a large model is cheaper overall because it gets it right the first time.
Common mistakes
- Assuming all small models are equally cheap; prices vary widely by provider and model.
- Ignoring retry costs: a cheap model that fails often can cost more than a reliable expensive one.
- Using a large model for every task out of habit, even when a small model would work fine.
