How to Choose an LLM

The core trade-off: quality vs price

Every major provider — OpenAI, Anthropic, Google, and the open-weight ecosystem (Llama, Mistral, Qwen and others) — ships a ladder of models, not one model. At the top sit frontier / flagship models: the strongest at reasoning, coding, and nuanced writing, and by far the most expensive. Below them are mid-tier models that trade a little quality for a big price cut, and then small / budget / nano models that are cheap, fast, and genuinely good enough for a large share of real tasks.

The mistake teams make is defaulting to the flagship for everything "to be safe." That is often paying 10–50× more for quality the task never needed. The skill isn't picking the best model — it's picking the cheapest model that still clears your quality bar for each specific job.

Match the model to the task

Difficulty, not importance, decides the tier. A high-stakes task can still be simple. Use this mapping as a starting point:

A powerful pattern is routing: send easy requests to a small model and only escalate the hard ones to a flagship. Most production traffic is easy, so routing captures most of the savings while protecting quality where it matters.

What the choice costs you

The price gap between tiers is enormous. As a 2026 rule of thumb, flagship models run roughly $1–5+ per million input tokens (output tokens more), while nano / budget tiers sit near $0.10 per million — a 10–50× swing on the exact same workload.

Worked example

Say you process 50 million input tokens a month of support-ticket classification:

• Flagship @ ~$3 / 1M: 50 × $3 = $150 / month
• Nano @ ~$0.10 / 1M: 50 × $0.10 = $5 / month

That's $145 saved every month (~30×) for a classification task a nano model does just as accurately. Multiply across every simple task in your stack and the flagship-by-default habit becomes the single biggest line item you can cut.

Practical selection steps

A repeatable process beats guessing from benchmarks — public leaderboards rarely reflect your data.

• 1. Define the quality bar. Write down what "good enough" means as something measurable: accuracy on a labelled set, a pass/fail rubric, or a human spot-check threshold. Without a bar you can't compare fairly.
• 2. Test 2–3 tiers on your task. Take 20–50 real examples and run them through a flagship, a mid, and a small model. Compare quality against price — often a cheaper tier ties the flagship on your specific job.
• 3. Route by difficulty. Put the cheapest passing model on the bulk of traffic and reserve the flagship for the genuinely hard slice, or for escalation when a cheap model returns low confidence.
• 4. Re-evaluate regularly. Prices fall and new tiers launch every few months. Today's flagship-only task may be solvable by next quarter's mid-tier at a tenth of the cost. Re-run your test set on a schedule.

Secondary factors to weigh

Once tier and quality fit, three more variables can change the decision:

• Reasoning tokens. "Thinking" / reasoning models generate hidden intermediate tokens billed as output. They lift quality on hard problems but can multiply cost — great for the flagship slice, wasteful for simple tasks.
• Context window. A larger window (128K, 200K, 1M) lets you feed more at once, but you pay for every token in context on every call. Don't buy a huge window you won't fill.
• Latency. Small models are usually faster. For interactive UX or high-throughput pipelines, a snappy small model can beat a slow flagship on user experience as well as price.

Read the mechanics behind these in How LLM API pricing works.

Frequently asked questions

Which LLM tier should I use for a simple task?

For classification, extraction, formatting, tagging or routing, a small or nano tier model is usually enough and costs a fraction of a flagship. Only escalate to a frontier model if the small model fails your quality bar on your own test data.

How much cheaper are budget LLMs than flagship models?

Nano and small tiers run around $0.10 per million input tokens while flagship models are commonly $1–5 or more. That is a 10–50× swing, so matching the tier to the task is one of the biggest cost levers you have.

How do I actually pick between two models?

Define a measurable quality bar, run 20–50 real examples from your task through two or three tiers, and pick the cheapest model that clears the bar. Re-test every few months because prices and model quality change fast.