Every "what does AI cost?" answer you've seen is either a vendor's sticker price or a guess. The real bill comes down to three numbers, and which one matters depends entirely on what you're building. So instead of one more article handing you a single figure, here are three free LLM cost calculators — one for each number — that run on your own inputs in the browser, no signup, no card. Pick the question that's actually yours and get a real answer in about thirty seconds.
This is the question for anyone wiring an LLM into a feature: one prompt in, one completion out, billed on tokens for both. The trap is that the headline per-million-token price tells you almost nothing until you multiply it by your prompt length and your output length — and the same call can be 50x cheaper or dearer depending on which model serves it. The fastest way to see that spread is to price one real prompt across every current model at once: try the free token cost calculator. It counts the tokens and shows the per-call cost on the 2026 frontier and budget models side by side, so you can pick the cheapest model that's actually good enough.
The moment your "one call" becomes an agent — plan, act, check the result, correct, retry — the math changes completely, because an agent re-sends its growing context on every step. A task that reads like one question can be ten model calls under the hood, and the input grows each turn, so cost compounds rather than adding. This is where bills surprise people. The honest way to size it is to model the loop itself: try the free AI agent cost calculator. It shows how context compounds across steps, then compares running everything on a frontier model against routing the routine steps to a cheap one — typically a 50-60% cut for the same work. If you want the concepts behind the number first, we wrote the long version in how much does an AI agent cost.
Once volume is high and steady, the question flips from "which API?" to "should I stop renting?" A per-token bill scales forever; a GPU you own is a fixed cost plus electricity. Somewhere there's a break-even — a monthly volume past which your own rig is the cheaper call — and it's usually closer than people assume for heavy workloads, further than they hope for light ones. Rather than guess, find your crossover: try the free self-host vs cloud calculator. It takes your token volume and a real GPU rig (capex plus sustained watts) and returns the break-even month and a three-year total cost of ownership against the cloud.
The pattern across all three: the model's sticker price is the smallest lever. What actually moves your bill is how many tokens you send, how many times the loop re-sends them, and whether high volume should live on hardware you own. Get those three right and you can run serious AI for a fraction of the headline cost.
Run those three calculators back to back and the same lesson keeps surfacing — most tasks don't need your most expensive model, but most setups send everything there anyway. That gap is the whole point of QADIR OS: it runs local brains and cloud brains through one router and sends each task to the cheapest model that can actually do it, falling back to a frontier model only when the job truly needs it. In the agent calculator that's the routed column; in real use it's frontier-grade output at close to budget cost. The calculators show you the savings; the OS is how you bank them without babysitting which model runs what.
"What does AI cost?" has no single answer — but it has three honest ones, and you can have all three in the time it took to read this. Price your call, model your agent loop, and find your self-host break-even with the free tools above. Then, if the routing lesson lands, the system that does it for you is one click away.
ABUZ8 is building QADIR OS — it routes every task to the cheapest model that can do it, so you get frontier output at close to local cost. The three cost calculators are free, no card. See QADIR OS, or join early access.