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Which AI model should you actually use?

By 7 Versions · July 14, 2026

The mistake is treating "AI" and "LLM" as the same word. A large language model is a remarkable reasoner over language. It is not a forecaster, not an optimizer, not a purpose-built perception system, and not the economical way to classify at volume. The pattern that actually wins on cost, speed, and accuracy is quieter: specialist models do the work, and a language model takes the order, routes it, and reports back in plain English.

That is our whole tagline. AI made output infinite. Judgement stayed finite. The value is not in generating more, it is in choosing better. So tell us what you are starting with and what you need back, and we will point you to the specialist that fits, in plain terms and in technical detail.

Find the right model

Two questions.

Start with what you have and what you need out of it. You get a plain-English answer and a real example, then the technical pick and the trap that catches most teams.

What are you starting with?

The short version

Twelve jobs where an LLM is the wrong tool.

Every row is something teams currently hand to an LLM. Every row has a better answer that is cheaper and faster, and once you have labelled data, usually more accurate.

The wider field

Language models are one family out of fourteen.

Every model family in this guide, sized by how many approaches it holds. Select any block to open it in the map underneath.

Large language models. The part everyone talks about. Everything else. The part that does most of the work.

Each block is one family. Its width and its number are how many approaches it holds. Select a block to open that family in the map below.

Six of the 145 approaches in this guide are language models. The other 139 are how you forecast demand, catch fraud, read an invoice, spot a defect, route a ticket, and decide who to call.

Now find yours.

Describe your problem in the search below and get a recommendation, or explore by hand: families run down the side, the answer you need runs across the top, and a bigger dot means more approaches at that intersection. The language model row is banded in amber.

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