Model Downgrade Advisor
Find tasks where a cheaper model does the job — and how much you save by routing them.
off your monthly bill by routing tasks to the cheapest model that holds quality — $390.00 drops to $182.00, saving $208.00/month.
| Task | Route to | Saves /mo |
|---|---|---|
| Boilerplate & scaffolding | Claude Haiku 4.5 downgrade | $64.00 |
| Docs & comments | Claude Haiku 4.5 downgrade | $24.00 |
| Test generation | Claude Haiku 4.5 downgrade | $48.00 |
| Refactoring | Claude Sonnet 4.5 downgrade | $40.00 |
| Debugging | Claude Sonnet 4.5 downgrade | $32.00 |
| Architecture & design | keep Claude Opus 4.8 | — |
Your routing rules
These render in FORG rule syntax — the rule engine applies them live to every request, so the downgrade happens automatically instead of by convention.
IF task=boilerplate THEN model=claude-haiku-4-5Pattern completion with low ambiguity — small models match frontier quality here.
IF task=docs THEN model=claude-haiku-4-5Summarization of code you provide; hallucination risk is low and reviewable.
IF task=tests THEN model=claude-haiku-4-5Mechanical once the interface is known; failures are caught by running them.
IF task=refactor THEN model=claude-sonnet-4-5Needs cross-file reasoning, but not frontier-grade planning.
IF task=debugging THEN model=claude-sonnet-4-5Hypothesis quality matters; mid-tier holds up with good context.
How it works
Most teams run every request through their strongest model because choosing per-task is friction. That habit is usually the single biggest line item on the AI bill. This advisor takes the model you currently use, your monthly token volume per task type, and returns a per-task verdict: keep, or downgrade to a named cheaper model — with the total savings as a percentage of your current bill and the reasoning shown for every recommendation.
The recommendations are deliberately conservative and asymmetric by cost-of-error. Boilerplate, docs and test generation route to a small model because their failures are cheap and loud: a bad test fails when you run it, a bad docstring gets caught in review. Refactoring and debugging route to a mid-tier model because they need cross-file reasoning but not frontier-grade planning. Architecture stays on your strongest model — its mistakes ship silently and compound. The advisor never recommends a model pricier than the one you already run.
Cost math uses a blended per-million-token rate assuming a 3:1 input-to-output split (75% input), which matches typical coding workloads, against prices verified on 2026-06-11. The savings number is exact for those assumptions; your real split shifts it a few points either way. What it does not model is the second-order win: cheaper models are also faster, so routing routine work down cuts latency on the majority of calls at the same time as it cuts cost.
The rule cards at the bottom are the point of the exercise. A spreadsheet conclusion ("we should use Haiku for docs") decays in weeks because nothing enforces it. A routing rule does not decay: FORG's rule engine evaluates IF task=docs THEN model=haikuon every live request, so the savings you compute here become a property of your infrastructure rather than of your team's discipline. Share the link to hand a teammate your exact scenario and the volumes behind it, then run the rules live with the engine to make the savings stick past the next deadline crunch.
Frequently asked questions
When is a small model like Haiku actually enough?
When the task is pattern completion rather than reasoning: boilerplate, scaffolding, docstrings, commit messages, test generation against a known interface, format conversion. These tasks have low ambiguity and cheap verification — you run the tests, you read the comment. Small models match frontier output quality on them at 5-25× lower cost, which is why they are the first candidates for routing.
What is the quality risk of downgrading, and how do I bound it?
The risk concentrates in tasks with expensive failure modes: architecture decisions, subtle debugging, security-sensitive changes. Bound it by routing only verifiable tasks downward (tests catch bad test-gen; review catches bad docs), keeping a fast escalation path to the stronger model, and never downgrading work whose errors ship silently. The advisor deliberately keeps architecture on your strongest model for this reason.
How does FORG automate this routing?
The rule cards under the results are real FORG rule-engine syntax. Instead of asking developers to remember which model to pick per task, FORG sits in the request path and applies the rules live — IF task=docs THEN model=haiku — so the downgrade is enforced infrastructure, not a convention that erodes the first time someone is in a hurry.
How do I measure whether a downgrade caused a quality regression?
Pick a verifiable signal per task before switching: test pass rate for test generation, review-rejection rate for docs and boilerplate, reopen rate for bug fixes. Run the cheap model on a slice of traffic, compare against baseline for two weeks, then ramp. Without per-task spend and outcome attribution you cannot run this experiment — which is the measurement layer FORG provides.
Turn this analysis into a live rule with the FORG rule engine — route models and enforce limits automatically.
Explore the rule engine