GPT-5.6 Now Comes in Three Prices. Here's How to Match Each One to the Right Job and Cut Your AI Bill

What did OpenAI just launch, and why does the price matter to your business?

OpenAI released GPT-5.6 in three tiers, and each one costs a different amount to run. GPT-5.6 Sol is the premium option at $5 per million input tokens and $30 per million output tokens. GPT-5.6 Terra sits in the middle at $2.50 and $15. GPT-5.6 Luna is the budget tier at $1 input and $6 output. A token is roughly three quarters of a word, so a million tokens is about 750,000 words in or out.

Here is the part most business owners miss. The three models are not three quality levels you pick once. They are three tools for three kinds of work. Sol costs five times more than Luna for the same volume of text. If you send every task to the top tier out of habit, you can pay five times what the job actually needs. The opportunity is not the new model. It is learning to send the right work to the right tier.

Why is using one model for everything the expensive mistake?

Most businesses that adopt AI pick one model and route everything through it. It feels simpler, and it is, until the invoice arrives. The problem is that the majority of AI tasks in a real business are routine and high volume. Sorting incoming emails into categories. Pulling a name and phone number out of a form. Tagging a support ticket. Drafting a first-pass reply that a human will edit anyway. None of these need a frontier model. They need a model that is fast, cheap, and good enough.

The expensive tier earns its price on a small slice of work. The final client-facing proposal. The nuanced reply to an unhappy customer. The summary a decision actually rests on. That is maybe ten to twenty percent of the volume in most workflows. When you pay premium rates on the other eighty percent, you are buying quality you never see.

This is what we mean when we say most AI-is-too-expensive complaints are really routing problems. The cost is not the technology. It is sending every job to the most expensive worker in the building.

How do you decide which task goes to which tier?

Start by splitting your AI work into two piles. The high-volume routine pile and the low-volume high-stakes pile.

The routine pile is anything repetitive where the output is structured, checkable, or gets reviewed by a person before it ships. Classifying, extracting, tagging, first drafts, internal notes. Send this to the budget tier, Luna in GPT-5.6 terms, or an equivalent cheap model from another provider. It handles the bulk of the volume at the lowest rate.

The high-stakes pile is anything that goes straight to a customer, drives a real decision, or requires genuine reasoning across messy inputs. This is where the premium tier, Sol, pays for itself, because a better answer here is worth real money and a worse one costs you a client.

The middle tier, Terra, is your default when you are unsure. It handles most everyday reasoning at half the top price.

A simple rule of thumb. If a human is going to check it, use the cheap model. If it ships without a human touching it, use the expensive one. Match the tool to the risk.

What does model routing look like in a real workflow?

In one engagement, a client was running every inbound lead through a single premium model to read the enquiry, categorise it, draft a reply, and flag anything urgent. It worked, but the cost climbed as volume grew, and most of that cost sat on the boring steps.

We split the workflow. A cheap model now reads each enquiry and sorts it into a category and urgency level, high volume and low stakes, checked by structure. A cheap model drafts the routine acknowledgements that follow a template. The premium model is reserved for the handful of complex or high-value enquiries that need a genuinely tailored response. Same quality where the customer feels it, a fraction of the cost on everything else. The lesson was not buy a cheaper model. It was stop paying premium rates for work that does not need them.

Do you need GPT-5.6 specifically, or is this about the approach?

The approach matters more than the brand. Every major AI provider now offers tiered pricing, a cheap fast model and an expensive capable one. The names change. The principle does not. Route the routine, high-volume steps to the cheapest model that clears your quality bar, and reserve the expensive model for the output that actually ships.

A few practical guardrails. Test the cheap tier on your real tasks before assuming it is not good enough, because it usually is for routine work. Keep a human check on anything irreversible or customer-facing. And measure the boring number, the cost per task and the time saved, because that is where the return shows up, not in using the flashiest model available.

Where should you start?

Pick the one workflow where you already use AI most, or the one manual process eating the most hours, and map which steps are routine and which are high stakes. That map alone usually reveals where money is leaking. If you would rather not untangle it solo, grab a free twenty-minute audit and we will show you where the hours, and the overspend, are hiding.