You can fine tune a small LLM with a few thousand examples in just a few hours for a few dollars. It can be a bit tricky to host, but if you share a rough idea of the volume and whether this needs to be real-time or batched, I could list some of the tradeoffs you'd think about.

Source: Consulted for a few companies to help them finetune a bunch of LLMs. Typical categorical / data extraction use cases would have ~10x fewer errors at 100x lower inference cost than using the OpenAI models at the time.

Labeling or categorization tasks like this are the bread and butter of small fine tuned models. Especially if you need outputs in a specific json format or whatever.

I did an experiment where I did very simple SFT on Mistral 7b and it was extremely good at converting receipt images into structured json outputs and I only used 1,000 examples. The difficulty is trying to get a diverse enough set of examples, evaling, etc.

If you have great data with simple input output pairs, you should really give it a shot.

if you add 2 spaces at the start of the line, you turn it into a code block

  like this

  | Model | DocType% | Year% | Subject% | In $/MTok |

  |----------------|----|-----|----|-------|

  | llama-70b -----| 83 |  98 | 96 | $0.72 |

  | gpt-oss-20b ---| 83 |  97 | 92 | $0.07 |

  | ministral-14b -| 84 | 100 | 90 | $0.20 |

  | gemma-4b ------| 75 |  93 | 91 | $0.04 |

  | glm-flash-30b -| 83 |  93 | 90 | $0.07 |

  | llama-1b ------| 47 |  90 | 58 | $0.10 |