OpenAI’s API pricing is no longer just a question of which model is cheapest. The current pricing structure creates a wider cost ladder: low-cost models for routine work, higher-priced models for harder or more output-heavy tasks, and discounts for workloads that can reuse context or run asynchronously. That gives developers more room to build, but it also makes token management a core product and finance discipline.
The real shift: a pricing ladder, not one default model
OpenAI’s pricing docs list a clear spread across the GPT-4.1 family: GPT-4.1 at $1.00 per 1M input tokens and $4.00 per 1M output tokens, GPT-4.1 mini at $0.20/$0.80, and GPT-4.1 nano at $0.05/$0.20 [2].
| Model | Listed input price | Listed output price | What it changes |
|---|---|---|---|
| GPT-4.1 | $1.00 per 1M tokens | $4.00 per 1M tokens | A stronger general option when quality matters more than minimum cost. |
| GPT-4.1 mini | $0.20 per 1M tokens | $0.80 per 1M tokens | A cheaper tier for high-volume, repeatable product features. |
| GPT-4.1 nano | $0.05 per 1M tokens | $0.20 per 1M tokens | A very low-cost tier for lightweight classification, extraction, routing, and similar tasks. |
That price gap changes how teams design AI products. Instead of sending every request to the strongest model, developers can test whether a cheaper model meets the quality bar and reserve more expensive models for ambiguous, high-value, or high-risk cases.
Developers are moving toward model routing
The new default pattern is cost-aware routing: use the cheapest model that can reliably complete the task, then escalate only when needed. For example, a product might use GPT-4.1 nano for simple classification, GPT-4.1 mini for customer-support drafts, and GPT-4.1 for requests that fail validation or require higher fidelity.
A practical routing system usually needs four pieces:
- Task segmentation: separate simple, repeatable work from complex reasoning or customer-critical workflows.




