A free model handles isolated prompts well. It stumbles on real codebases. This article maps exactly where it breaks.
The experiment: migrate a small Express app to Fastify using only a free model endpoint. Six steps, one real codebase, zero cherry-picking. The results reveal a pattern worth knowing before you trust a free tier with production code.
The Experiment Design
The target application is small but real. It has five routes, two middleware functions, and one error handler. Total size: 300 lines. The task: convert it to Fastify while preserving behavior.
The model endpoint came from MonkeyCode, an open-source project offering free model access and a free server option. The current README reports a free allowance of 10 million tokens. Verify the current numbers before relying on them.
Disclosure: This article was prepared as part of MonkeyCode's product outreach.
The migration was split into six discrete steps. Each step was a separate prompt. Each step was evaluated as pass, partial, or fail.
| Step | Task | Result |
|---|---|---|
| 1 | Summarize the route structure | Pass |
| 2 | Convert middleware registration | Partial |
| 3 | Convert route handlers | Fail |
| 4 | Convert error handling | Fail |
| 5 | Update server startup | Pass |
| 6 | Generate a test suite | Fail |
The failure points cluster around framework API differences. That clustering is the finding.
Step 1: Route Structure Summary
The first prompt asked for a route inventory. The model produced an accurate list of all five routes with their methods and paths.
This is the free tier's strength: reading and summarizing. It requires no transformation, just comprehension. The output was usable without edits.
Step 2: Middleware Conversion
The second prompt asked to convert two Express middleware functions to Fastify plugins.
The result was partial. One middleware converted cleanly. The second one lost its next() call, which would hang the request. The error was subtle and easy to miss in review.
Step 3: Route Handler Conversion
This step produced the first hard failure. Express uses req.params and req.query as plain objects. Fastify uses the same names but with different parsing rules.
// Express style (what the model generated)
app.get('/user/:id', (req, res) => {
res.json({ id: req.params.id });
});
// Fastify style (what was required)
app.get('/user/:id', (request, reply) => {
reply.send({ id: request.params.id });
});
The model mixed the two conventions. It also missed that Fastify requires reply.send() instead of res.json(). The output compiled but would return empty responses at runtime.
Step 4: Error Handling Conversion
Express error handlers use a four-argument signature. Fastify uses a different error-handling model. The model produced an Express-style handler inside a Fastify app.
// Express error handler (what the model generated)
app.use((err, req, res, next) => {
res.status(500).send(err.message);
});
// Fastify error handler (what was required)
app.setErrorHandler((error, request, reply) => {
reply.status(500).send(error.message);
});
The result would silently swallow errors. This is the most dangerous failure mode because it does not crash. It just fails quietly in production.
Step 5: Server Startup
The startup logic converted cleanly. Fastify's listen method is close enough to Express's that the model handled it correctly.
This step was trivial. It also proves the model is not uniformly bad. It fails on specific patterns, not on everything.
Step 6: Test Suite Generation
The final prompt asked for a test suite that verifies behavior equivalence between the old and new apps.
// What the model generated: status-only checks
test('GET /user/:id returns 200', async () => {
const res = await app.inject({ method: 'GET', url: '/user/1' });
expect(res.statusCode).toBe(200);
});
// What was required: behavior checks
test('GET /user/:id returns the user', async () => {
const res = await app.inject({ method: 'GET', url: '/user/1' });
expect(res.json()).toEqual({ id: '1' });
});
The model generated tests that checked for HTTP 200 responses. It did not check response bodies, status codes for error paths, or middleware behavior. The tests would pass even if the migration broke everything.
The Failure Patterns
Three patterns explain all three failures.
First, framework API differences are invisible to the model. It sees req and res and assumes they behave identically. It does not consult the target framework's documentation.
Second, error handling is consistently wrong. Error paths are underrepresented in training data. The model defaults to the most common pattern, which is Express's.
Third, generated tests validate "it runs" instead of "it behaves the same." This is a fundamental blind spot. The model cannot reason about behavioral equivalence without running the code.
The Real Cost of Free
The time accounting is uncomfortable. In this run, six steps took 40 minutes of model calls and review. Three failures required 45 minutes of manual fixes. Total: 85 minutes.
Manual migration of the same app took 60 minutes. The free model made the task slower, not faster. It added review overhead and introduced subtle bugs.
This is the hidden cost of free tiers. The token price is zero. The review price is not.
Where the Free Tier Still Works
The experiment does not condemn free models. It defines their limits.
Free model access works for: code explanation, single-file scripts, test scaffolding, and documentation drafts. These tasks are self-contained and low-risk.
It fails for: cross-file refactors, framework migrations, and behavior-preserving transformations. These tasks require holding a system model in context. That is exactly what free tiers struggle with.
A Practical Rule
Use free model access for tasks you can verify in seconds. Avoid it for tasks where a wrong answer is invisible.
A wrong route handler compiles. A wrong error handler runs. A wrong test suite passes. Verification is the only safety net, and free tiers make verification expensive.
The Server Question
MonkeyCode also offers a free server option. The same logic applies. A free server is fine for prototyping and local experiments. It is not a substitute for a production environment.
Check the project's current documentation for details on the free server's limits. Treat any number you read as a snapshot, not a promise.
Who Should Skip This Approach
Skip free-tier model access if your task involves multiple files. Skip it if you need behavioral equivalence. Skip it if you cannot review every line of generated code.
The free tier is a tool for bounded tasks. Use it where failure is cheap and visible. Keep it away from migrations and refactors.
Run your own version of this experiment. Pick a small real codebase, split it into steps, and record where the model fails. The failure map you get will be more valuable than any benchmark score.
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