Model cost comparison

Compare the same workload across active AI models.

Normalize input, output and monthly volume, then sort by estimated list-price cost. Cost is not a quality score.

Representative prompt

Exact input tokens optional

Output tokens

Requests per month

Tokenizer profile

Provider

Preferred tier

Pricing date

Model	Per request	Monthly	vs cheapest	Applied pricing

How to use this table

Shortlist several models, then run the same representative evaluation set against each one. Compare cost per successful task—not cost per raw request.

Why outputs matter

Output and reasoning tokens are frequently more expensive than input. A model that completes the task in fewer generated tokens can be cheaper despite a higher list price.