## Why
We pay for AI transaction categorization on Gemini, but we couldn't tell
which model produced any given suggestion. The `gemini-flash-latest`
alias used by categorization is **floating** — it silently drifted from
the `flash-lite` tier up to `gemini-3.5-flash`, which is now the
dominant cost on the Gemini dashboard. Before swapping categorization to
a cheaper pinned model we need to measure accuracy per model, and for
that we have to know which model labelled each row.
We already store the AI-suggested category and its confidence on
`transactions`; this adds the missing third axis: the model.
## What
- New nullable `ai_model` column on `transactions`, written by
`CategorizeTransactions::recordOutcome` from
`config('ai_categorization.model')` whenever the model produces a
suggestion (above or below the label bar).
- The migration backfills existing AI-categorized rows
(`ai_suggested_category_at` not null) with `gemini-3.5-flash` — the
model `gemini-flash-latest` resolved to when those suggestions were
made. Done as a raw update so it doesn't bump every row's `updated_at`.
- `ai_model` added to `$fillable` and to `$hidden` (pure ops/measurement
metadata, no need to ship it to the frontend).
## Testing
- Extended `CategorizeTransactionsTest` to assert `ai_model` is stored
on both the auto-applied and below-bar paths.
- `php artisan test tests/Feature/Ai/CategorizeTransactionsTest.php` → 5
passed.
## Follow-ups (not in this PR)
- Pin `AI_CATEGORIZATION_MODEL` to an explicit cheaper tier (mirror the
existing `AI_SUGGESTIONS_MODEL=gemini-2.5-flash-lite`) and kill the
floating alias in the config defaults.
- Capture token usage from the SDK response for cost tracking.
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