Find out what switching to a cheaper model saves you
› I want to know whether switching AI models would save me money without hurting quality. Act as a careful cost analyst and ask me for anything missing before you calculate.
My current setup:
- Current model and its price per million tokens: [input price] / [output price]
- Candidate model and its price per million tokens: [input price] / [output price]
- Typical monthly usage: [input tokens] input, [output tokens] output (or describe the task and roughly how many times it runs)
- Share of my input that repeats every call, like a long system prompt: [percent], and cached input prices if either model offers them: [prices]
- What the task is: [describe it, e.g. an agent that fills in web forms]
Do this:
1. Calculate my monthly cost on each model, with and without caching. Show the math in a small table.
2. Point out that a cheaper model can use more tokens per finished task, and estimate the break-even: how many more tokens per task the candidate could use before it stops saving money.
3. Design a fair test I can run this week: 20 to 50 real examples, what to measure (cost per finished task, error rate, time), and what result would justify switching.
4. List three risks of switching for this specific task and one guardrail for each.
Keep it plain. No jargon without a one-line explanation.
Works in any AI chat. In Magai, run it against several models at once.