Route AI Coding Tasks by Risk: A Free-Tier-First Workflow You Can Actually Measure

Most discussions about AI coding tools start with “which model is best?” I’ve found that’s the wrong first question. The better question is: which of my tasks actually need the strongest model, and which ones don’t?

In my earlier posts I wrote about building a small evaluation suite for AI coding models and a falsification loop for reviewing AI-generated refactors. This post is the missing piece between them: a routing layer that decides, per task, whether a free-tier model is good enough — and a way to measure whether that decision was right, instead of trusting vibes.

The problem: paying frontier prices for boilerplate work

When every prompt goes to the most expensive model by default, two things happen:

1. You burn budget on tasks a weaker model handles fine (renaming, boilerplate, docstrings, simple test generation).
2. You never build intuition for where the strong model genuinely matters, because you never see the failure distribution of the cheap one.

The fix isn’t a blog-post benchmark. It’s a per-task routing rule plus a log you can audit weekly.

Step 1: Classify tasks by blast radius, not difficulty

Difficulty is subjective. Blast radius — what breaks if the output is wrong and you don’t catch it — is not. I use three tiers:

Tier Task examples Failure cost Default route Low Rename/refactor with compiler backing, boilerplate, doc comments, unit test scaffolding, commit message drafts Caught by compiler/CI in seconds Free/cheap model Medium New function in an existing module, bug fix with a clear reproducer, small migration script Caught by code review or tests, costs an hour Free model first, escalate on failure High Concurrency changes, auth/payment logic, schema migrations on live data, security-sensitive parsing May reach production silently Strongest available model + mandatory human review

Two rules make this table work:

• Escalation is cheap, so bias toward the free tier. If the free model’s output fails your checks, you escalate that one task. You lose minutes, not money.
• Tier High is non-negotiable. Anything whose failure mode is “silent wrongness in production” never starts on the free tier, no matter how confident you feel.

Step 2: A gate that every AI output must pass

Routing only works if each tier has an objective accept/reject gate. Mine is:

#!/usr/bin/env bash
# gate.sh — run after applying any AI-generated change.
# Exit 0 = accept, non-zero = escalate to a stronger model or do it by hand.
set -e

echo "== typecheck =="
npx tsc --noEmit          # swap for: mypy, go build, cargo check...

echo "== existing tests =="
npm test -- --silent      # must pass with zero new failures

echo "== diff sanity =="
# Reject diffs that touch files outside the task's declared scope.
# I pass the allowed path prefix as $1, e.g. ./gate.sh src/billing/
git diff --name-only | grep -v "^$1" && {
  echo "FAIL: change escaped declared scope"; exit 1;
} || true

echo "PASS"

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The scope check matters more than it looks. In my experience the most common free-tier failure isn’t wrong logic — it’s the model “helpfully” editing files you didn’t ask about. A one-line git diff filter catches that class entirely.

Step 3: Log every routed task, review weekly

This is the part that turns routing from a superstition into a measurement. One line of JSON per task:

{"date":"2026-08-11","tier":"low","route":"free","gate":"pass","escalated":false,"minutes":6}
{"date":"2026-08-11","tier":"medium","route":"free","gate":"fail","escalated":true,"minutes":19}
{"date":"2026-08-11","tier":"high","route":"strong","gate":"pass","escalated":false,"minutes":31}

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After two weeks, answer three questions from the log:

1. What percentage of free-routed tasks passed the gate on the first attempt? (My threshold: if it drops below ~60% for a tier, that tier’s routing rule is wrong.)
2. When tasks escalated, did the strong model actually fix it, or was the task misclassified as Medium when it was really High?
3. Are High-tier tasks sneaking into the free route? (Any “yes” here is a process bug, fix the table, not the model.)

This is deliberately the same philosophy as my earlier evaluation-suite post: small, runnable, and honest about failure counts instead of average-case impressions.

Where the free tier comes from

Routing toward a free tier only helps if you actually have one. Disclosure: This article was prepared as part of MonkeyCode’s product outreach. MonkeyCode currently offers free model access and a free server option, which is what makes a free-first routing rule practical to run as an individual — the “free route” in the table above is a real default rather than a hypothetical one, and the free server means the logging/gate scripts can run somewhere other than your laptop. I won’t quote specific model names, quotas, or performance numbers here, because those change and you should verify them yourself against the current offering; the workflow in this post is deliberately provider-agnostic, and the gate + log will tell you within two weeks whether the free tier is pulling its weight for your codebase.

Limitations, and who shouldn’t do this

• Tiny codebases and solo weekend projects: if you write ten AI-assisted tasks a week, the log overhead exceeds the savings. Just use whatever model and move on.
• Domains where correctness is unverifiable by tests (e.g., ML feature engineering without ground truth, UX copy): the gate script can’t catch silent wrongness, so routing by gate results gives false confidence.
• Regulated or security-critical code: the High tier in my table should probably be “no AI generation at all, AI-assisted review only.” A routing table is not a compliance story.
• The classification itself is a judgment call. Expect to misclassify for the first two weeks; the weekly log review exists precisely to correct that.

The takeaway

“Which model is best” is a benchmark question. “Which model is sufficient for this task, and how would I know if it wasn’t” is an engineering question. A blast-radius table, an objective gate, and a one-line-per-task log will answer it for your own workflow in about two weeks — and whatever free tier you route to, you’ll know exactly how much it’s earning its place. If you try this, I’d genuinely like to hear what your pass-rate numbers look like; that’s the dataset nobody publishes.