Quick answer
To route simple tasks to cheaper AI models without losing quality, list the AI jobs you do regularly, sort them into simple, medium and hard, and send each group to the cheapest model that passes a small quality test. Summaries, tagging, formatting and short replies usually work well on small, fast models. Complex reasoning, important writing and tricky code stay on a premium model. Write the rules down, add an automatic step up to a stronger model when the cheap one is unsure, and spot-check results each week. Google is reportedly building this kind of smart routing into its own systems, but you can start today.
Key takeaways
- Most everyday AI jobs don’t need a top model. Small models handle summaries, sorting and formatting well.
- Sort tasks into simple, medium and hard before you pick models.
- Test each route on 10 real examples before switching for good.
- Always include a step up: if the cheap model is unsure or fails a check, pass the job to a stronger one.
- Review a sample each week so quality doesn’t drift quietly.
📘 Main article: This guide follows our Anthropic recursive self-improvement and AI news round-up, which covers Google’s reported smart routing work and why new frontier models may cost more.
In this guide
- What does routing tasks to cheaper models mean?
- Which tasks can go to a cheaper model?
- How do you route simple tasks to cheaper AI models?
- What do simple routing rules look like?
- How do you check you aren’t losing quality?
- Which tools can do the routing for you?
- Frequently asked questions
- The bottom line
- Related posts
New frontier models are impressive, but they aren’t cheap, and usage limits on AI plans keep changing. If every job goes to your most powerful model, you are probably paying for brainpower you don’t need.
This guide shows you how to route simple tasks to cheaper AI models step by step, so you save money and time while keeping the results you rely on. It builds on the routing news in our main article.
What does routing tasks to cheaper models mean?
Routing means each AI task goes to a model chosen for that job, rather than everything going to one model. Think of it as triage: quick, routine work goes to a fast, low-cost model, and only the demanding work goes to the expensive one.
According to a leak covered by WorldofAI, Google is working on a system that does this automatically, judging how complex each prompt is and picking the model to match. The goal is roughly the same accuracy with lower cost and faster replies.
Which tasks can go to a cheaper model?
| Task type | Examples | Usually suits |
|---|---|---|
| Simple | Summarising emails, tagging leads, fixing formatting, extracting names and dates, short replies | Small, fast model |
| Medium | Blog outlines, product descriptions, routine code changes, customer support drafts | Mid-range model |
| Hard | Strategy, complex code, legal or financial documents, final copy that goes out under your name | Premium model |
Your own list will look different. The point is to be honest about which jobs really need deep reasoning.
💡 Tip: If a task has a clear right answer you can check, such as “Is this email a complaint, yes or no?”, it is usually a good candidate for a cheaper model.
How do you route simple tasks to cheaper AI models?
- List your regular AI jobs. Look back over a week of use and write down every type of task, with roughly how often you do it.
- Label each one simple, medium or hard using the table above.
- Pick one model per level. Use the smallest model from your current provider for simple, its standard model for medium and its top model for hard.
- Test each simple task on 10 real examples. Run them through the cheap model and compare with what your premium model produced.
- Keep the cheaper route if it passes. A good rule is that at least 9 out of 10 results should be usable without edits.
- Add a step up. Tell the cheap model to reply “ESCALATE” when it isn’t confident, or add a simple check, and send those jobs to the stronger model.
- Write the rules down in a shared document or your automation tool, so everyone and every workflow follows them.
Catch up on the news behind this guide: our Anthropic recursive self-improvement round-up covers smart routing, new models and why bills may rise.
What do simple routing rules look like?
Start with plain rules you can read at a glance. For example:
- If the task is classify, tag, extract or reformat, use the small model.
- If the input is under one page and the output is a short draft, use the mid-range model.
- If the output is published, sent to a customer, or involves money, law or code going live, use the premium model and a human check.
- If the small model says it is unsure, or the result fails a format check, send it up one level.
In a tool like Zapier or Make, this can be as simple as a filter or path step that chooses which AI step runs. Some people add a tiny first step where a cheap model reads the request and labels it simple, medium or hard; the label then decides the route.
⚠️ Warning: Don’t route sensitive customer data to a model or provider you haven’t checked. Cheaper isn’t better if it breaks your privacy promises.
How do you check you aren’t losing quality?
- Keep a small test set of 10 to 20 real examples for each task, and rerun it whenever you change models.
- Spot-check weekly: read five random outputs from each cheap route.
- Track step-ups: if more than about one in five jobs gets escalated, the task may belong at the next level.
- Watch for complaints or rework. Time spent fixing outputs is a hidden cost.
For writing tasks, a useful middle path is to draft with a cheap model and polish with a premium one. Our guide on drafting with a cheap AI model and refining with a premium one walks through it.
Which tools can do the routing for you?
You can do this by hand, in an automation tool, or with a routing service.
| Option | Good for | Effort |
|---|---|---|
| Manual choice in your AI app | Individuals who switch models per task | Low |
| Paths or filters in Zapier or Make | Small businesses with repeat workflows | Low to medium |
| Model routers and AI gateways | Teams calling AI through an API | Medium |
| Built-in automatic routing from providers | Anyone, once available | Very low |
If your AI plan’s limits have been cut, routing also stretches your allowance further. See what to do when your AI plan’s usage limits are cut, and if you run agents through an API, pair routing with prompt caching.
Frequently asked questions
Will cheaper AI models give worse results?
For hard tasks, often yes. For simple tasks such as summarising, tagging and formatting, small models usually do just as well. Test on your own examples to be sure.
How much can routing save?
It depends on your mix of tasks and your provider’s prices. Small models often cost a fraction of premium ones, so moving routine, high-volume work tends to make the biggest difference.
What is an AI model router?
A tool or step that looks at each request and decides which model should handle it, usually based on rules or a quick judgement by a small model.
Do I need to code to route tasks?
No. You can choose models by hand, or use paths and filters in no-code tools like Zapier or Make.
Is Google’s smart routing available now?
Not as far as we know. It was reported as a leak in WorldofAI’s round-up, so treat it as unconfirmed.
The bottom line
You don’t need your most powerful AI model for every job. Sort your tasks, test cheaper models on real examples, add a step up for anything tricky and check quality weekly. Your next step: pick your single most frequent simple AI task and test it on a small model this week.
Related posts
- Anthropic Recursive Self-Improvement: What the IPO Filing Change Means, Plus Fable 5.5 and This Week’s AI News
- How to Draft With a Cheap AI Model and Refine With a Premium One
- What to Do When Your AI Plan’s Usage Limits Are Cut
