How to test two Claude models on one real task and compare cost

Picking a model from a menu is guesswork until you test it on your own work. This short method helps a team compare two Claude models fairly, using one real task and a simple cost record.

Step 1: Pick one real task

Choose a job your team does repeatedly, such as summarizing a contract, drafting a client reply, or reviewing a spreadsheet. Avoid toy examples. A real task shows real differences.

Step 2: Define what "good" looks like

Write a short checklist before you run anything. For example:

• Are the facts correct?
• Is the format what you asked for?
• Would a colleague accept it without edits?

A checklist keeps the comparison honest and lets the model check its own work too.

Step 3: Set up two runs

1. Run the task on your current default, such as Opus 5.5 at medium thinking.
2. Run the same task on a second option. Change only one thing: either the model (for example Sonnet 5.5) or the thinking level, not both.

Changing one variable at a time tells you what actually made the difference.

Step 4: Record the cost

Note the tokens used and the price per token for each run. Your cost is tokens times price, so a cheaper model that needs several retries can cost more than a pricier one that finishes in one pass.

Step 5: Compare cost per finished job

If the cheaper run passes the checklist, use it. If it misses a few items, try the pattern of running low first and redoing only the misses.

Step 6: Write down the decision

Record which model and thinking level you chose, for which task, and why. Revisit it when models change. Our post on choosing a Claude model explains the trade-offs in more detail.

If you want help building this kind of testing habit into your organization's AI strategy, our team can help.