The LLM: choosing the engine (it's a buyer's market)
What it is — in one coffee-break
The LLM — large language model — is the engine everything else in this table orchestrates: the thing that actually reads, reasons and writes. Every other element exists to feed it (context, embeddings, retrieval), steer it (prompts, guardrails), connect it (function calling, protocols) or check it (tracing, evaluations). Choosing which engine to run, at what price, is the most-revisited decision in any AI stack — and in 2026 it's a buyer's market.
When you actually need it (and when you don't)
The real question isn't whether you need an LLM — if you're reading this track, something in your business already touches one. The question is which class of model for which job. Our Model Pricing table groups them the way buyers should think: frontier models (the judgment tier — $5–10 per million input tokens) for work where being wrong is expensive; heavy models at a fraction of that for daily production; medium and light models for high-volume simple tasks at prices down to cents per million tokens.
The pattern that saves the most money: match the model to the task, not to the marketing. A €0.10 model classifying support emails all day plus a frontier model reviewing the hard 5% beats one expensive model doing everything — this site runs on exactly that principle, with local open-weight models doing the routine work. And because model quality has commoditized faster than anything else in the stack (a theme we've covered in the deep-dives), the switching cost you should actually fear isn't the model — it's everything you build around it.
How to recognize good vs bad implementations
In the audits, the LLM element (Lg) measures the model surface a tool gives you: quality, choice, and transparency about what's running. The 9-club — ChatGPT, Claude, Gemini, DeepSeek — all pair frontier-class engines with real model choice. More interesting are the tools built ON models: Cursor scores Lg 9 because it lets you pick from every major provider, while some assistants score far lower for hiding which model answers and swapping it without notice. Two tells for any tool: can you choose (or at least see) the model, and does the vendor publish what a task costs?
What this costs
This is the best-documented cost in AI — every serious vendor publishes per-token prices, and we track all of them, sourced and dated, on the Model Pricing tab, including a calculator for your own workload. Two rules of thumb: output tokens cost 3–6× input tokens, and "thinking" models bill their reasoning as output — so a reasoning model can cost several times its sticker price on hard problems. Open-weight models flip the equation: zero per-token cost, but you pay in hardware and operations.
Where to see it scored
Lg scores across the table: Gemini (9), Cursor (9), Suno (8.5 — the engine is the product), Meta Llama (5.5). For head-to-head engine choices, use the AI Versus comparator.