Price statistics increasingly draw on scanner, web-scraped and receipt data whose product names are short, noisy and carry no standard code, so they must be coded to consumption categories. We release a synthetic benchmark of six categories with character-level noise, which favours character features, and hard negatives, and compare seven models trained from scratch (four logistic regressions on word or character n-grams, an MLP, a CNN and an LSTM) and two pretrained encoders under one matched protocol of 30 category-seed cells, reporting wins, losses and ties rather than mean differences alone. A character n-gram logistic regression is the most accurate model in every category and wins every paired comparison against the other models trained from scratch; the CNN and LSTM, once trained to the same stopping rule, are not reliably worse than the word-level models but cost 212 and 258 times as much to train as unigram bag-of-words. A fine-tuned DistilBERT comes within half a point of the character model without passing it, at about 460 times its single-thread CPU inference cost. Character features reduce inference throughput 4.6-fold relative to unigram bag-of-words. A rule-based prefix-tree stage admits 75-86% of positive items, so in a cascade it bounds the recall of the whole pipeline. A Monte Carlo study of label aggregation with simulated annotators shows that an additive reliability weight barely improves on majority vote while Dawid-Skene aggregation recovers labels markedly better, and that this difference carries through to the character n-gram classifier. Sensitivity checks cover a planted shortcut, generator noise, training budget and annotator population. The benchmark is synthetic and English-language because the production data are under non-disclosure. Code, data and scripts are released; accuracies of models trained from scratch reproduce exactly, timings as ratios.

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