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

> Source: <https://dev.to/datars_7274/route-ai-coding-tasks-by-risk-a-free-tier-first-workflow-you-can-actually-measure-3p50>
> Published: 2026-08-13 03:21:46+00:00

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.

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

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

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:

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

``` bash
#!/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"
```

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.

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}
```

After two weeks, answer three questions from the log:

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

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.

"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.