How to Cut Your AI Bill Without Losing Output Quality
Five prompts, ten of your own tasks, and a one page rule for which model gets which job
Hey Adopter,
is your company running on one default AI model that handles everything? Contract review and bullet point summaries, same token price. In this newsletter, you will find out how to test different models on your tasks and be more efficient as a company. The big ones are already doing it.
Most of what you send it is not hard. You pay top rates anyway.
The bill grows. Finance asks why. Nobody has an answer, because nobody has run the same task on two models and compared the results.
You are guessing. Guessing is expensive.
The waste sits in routine work. Extraction, classification, cleaning up notes, drafting standard replies. All charged at the top of the range, because the top of the range is already selected and switching for one task feels like effort.
Same thing at your desk. One model for everything means your daily limit goes on admin, and you have nothing left when the hard problem lands.
Public benchmarks will not settle this for you. They score someone else’s tasks on someone else’s data. Your work is the only test that counts, and testing it is easier than people assume.
The fix takes an afternoon.
I built five prompts. Each one makes a model show its working on a different failure mode:
How deep it reasons before it commits
How many tokens it burns getting there
Whether it invents sources
Whether it changes its answer when the audience changes
Which tier any new task belongs in
Run them on two models. Score what comes back. You end up with a routing rule based on your own work, a list of jobs the cheap model handles fine, and a number you hand to finance.
No engineering required. No new tool to buy. Two browser tabs and a scoring sheet.
All five are below, ready to copy.
Take the whole pack instead of copying
Every prompt I use lives in Right Click Prompt. This pack consists of seven prompts, two more than this edition covers. One builds your task list for you. One scores two model outputs head to head.
Scan the code or open rcp.ad/benchmark. Import once, it sits in every AI tab you open, and shares with your team in a click.
Over 3,000 people on it now. Thank you, the support has been overwhelming. There is a discounted lifetime deal running at the moment.
What the gap is worth
Ramp routes more than 100 internal AI use cases through a router they built. Their Router page reports 30% lower LLM costs at about 30 ms of added latency, across 2.75 trillion tokens a month.
Glean CEO Arvind Jain estimates around 95% of enterprise AI usage still runs on the most expensive frontier models. Cognition CEO Scott Wu puts the gain on routine work at five to ten times better cost efficiency. Both spoke to CNBC. Wu’s example is asking which president came third. Every model says Thomas Jefferson. You paid frontier rates to hear it.
On waste specifically, OckBench tested 49 model settings and found top open source models now match commercial ones on accuracy while spending up to 26 times more tokens. Same answer, very different bill.
Those numbers are someone else’s work. Here is how you get your own.
Step one, build the task list
Pull eight to twelve real tasks from the last fortnight. Mix routine jobs with one analytical piece, one thing that went to a client, and one where a wrong answer would have cost you money.
This is the task list I walk business owners through building in the seven lessons edition, alongside how to turn it into a standing benchmark. Build it once. You reuse it every time a model launches.
Then pick two models. Your current default, and a new model you consider switching to. Run each prompt on both. Do not help either one.
