Choose which AI model to use for each task without overpaying

Who it is for

Anyone who uses AI daily or has it automated in some workflow, and watches the bill go up without knowing whether they are using a model that is too big for simple tasks or too small for the important ones.

The problem

Every provider offers several models with similar names and very different prices. For convenience, one model gets used for everything: you overpay on the simple tasks and lose quality on the hard ones.

Ingredients

• The list of the ten tasks where you use AI the most (by hand or automated)
• The official models page of the provider you use, open in the browser
• Twenty minutes and a spreadsheet

Step by step

1. 1

   List your tasks and classify them

   For each one: is it hard or simple? Does a mistake cost a lot or get fixed for free? Does it repeat a hundred times a day or once a week? Three columns.

2. 2

   Apply the rule

   Hard or with a high cost of error: the most capable model. Simple and repetitive: the fastest and cheapest. In between: the middle one. The source describes each model by capability, speed and cost; use that description, not the names.

3. 3

   Test the simple tasks with the cheap model

   Ten real examples. If it gets nine right, stay there. If it fails three, go up one level for that task only.

4. 4

   Write the table and share it

   Task, model, why. One page. It is your company's AI policy, without sounding like a policy.

5. 5

   Review it when a new model comes out

   Every release moves the frontier: what was expensive gets cheaper, what was impossible becomes possible. Half an hour each time the official page changes.

Replace what is in brackets with your company details, or use the guided mode above: it asks you and fills it in for you.

# Role
You are the AI cost advisor for [COMPANY NAME]. You recommend with business judgment: quality where it matters, savings where nobody notices.

# Context
These are the tasks where we use AI, with their difficulty, the cost of a mistake and the frequency: [LIST OF TASKS WITH DIFFICULTY, COST OF ERROR AND FREQUENCY]. We use [PROVIDER OR PROVIDERS, FOR EXAMPLE "Claude and ChatGPT"]. Below I paste the current official description of their models.

# Task
Assign a model to each task following the rule: hard or with a costly error → the most capable; simple and repetitive → the fastest and cheapest; in between → the middle one. Explain each assignment in one line and flag the tasks where it is worth testing the cheap model with ten examples before deciding.

# Output format
Table: task · recommended model · why · test first (yes/no). Below, the rule written in two sentences so the team can use it with new tasks.

# Examples
Task: "Classify 300 emails a day by urgency" → fast and cheap model; simple, repetitive, correctable error; test first: yes.

What usually goes wrong

• ✕Choosing by name or by hype. Choose by the capability, speed and cost description the official source gives.
• ✕Optimizing without volume. With few queries a day, the savings do not exist and the complexity does.
• ✕Not reviewing when a new model comes out. The table from six months ago may be overpaying.