AI API Pricing Comparison

Comparing AI API prices can be confusing because providers use different models, capabilities, context limits, caching systems, and pricing structures.

How the estimate is calculated

The most common mistake is comparing only the price of one million input tokens.

Imagine Model A costs $1 per million input tokens while Model B costs $3. Model A looks cheaper. However, Model A may generate longer answers or require two requests to solve tasks that Model B completes with one.

What can change the total

A better comparison includes:

Input token price Output token price Cached-input price Context-window size Long-context premiums Batch discounts Reasoning-token treatment Tool charges Average task success rate Latency

Plan with a realistic usage example

Current examples demonstrate how different the pricing structures can be. The original GPT-5 API model lists $1.25 input and $10 output per million tokens. Claude Sonnet 4.6 starts at $3 input and $15 output. Gemini 2.5 Flash lists $0.30 text input and $2.50 output per million tokens.

These numbers should not be interpreted as a ranking of model quality.

Practical checks before you proceed

Instead, run a representative evaluation set through candidate models. Measure how often each produces an acceptable answer and how many tokens it uses.

Then calculate cost per successful task.

Practical checks before you proceed

Pricing changes frequently, so static comparison pages should always display a last-updated date and link readers toward official provider pricing before they make purchasing decisions.