Cheap AI API: how to get AI models at discounted prices

Frontier models keep getting better, and their bills keep getting bigger. A team that started with a few dollars of experiments can find itself paying thousands a month once a feature ships, and the usual reaction is to hunt for something cheaper. The trouble is that "cheap" means different things depending on how you use AI, and several of the popular options only look cheap until you try to build on them.

If you're new to how these prices work, start with what a token is. This guide walks through every realistic route to lower-cost AI models, what each is good for, and where it breaks down. Prices below are real figures from the GreenTokens catalogue at the time of writing, quoted per million tokens.

First, decide what "cheap" should mean

The price per token is only half the story. What you actually care about is the cost of getting a task done: summarising a document, answering a support ticket, reviewing a pull request. A model that costs twice as much per token but finishes the job in one attempt can be cheaper than a bargain model that needs three retries and a human to fix the result.

So the goal is the lowest cost per successful task, at the quality your product needs. Keep that in mind as you read the options below, because each one moves a different part of that equation.

Option 1: Pay the provider directly

Signing up with each model provider and paying list price is the default. You get the full feature set and the provider's own infrastructure, and you pay the official rate. For Claude Sonnet 5 that's $2 per million input tokens and $10 per million output tokens; for GPT-6 Sol it's $2 and $10.

The hidden cost is operational. Every provider means another account, another API key, another invoice and often another SDK format. Once you use models from three or four labs, which most teams eventually do, that overhead adds up, and none of it makes your tokens any cheaper.

Option 2: Consumer subscriptions

Monthly chat subscriptions look like the best value on paper: a flat fee for heavy use of a top model. But they're built for a person typing into a chat window, not for software. They don't give you an API key your application can call, usage is capped in ways that don't suit automated traffic, and using them to power a product usually breaks their terms.

If you're a single person chatting with a model, a subscription can be the cheapest way in. If you're building anything, it isn't an option at all.

Option 3: Free tiers and trial credits

Many providers offer free credits or rate-limited free tiers. They're genuinely useful for prototyping, comparing models and learning an API. They are not a plan for production: limits are low, credits run out, and terms change without much warning. Treat free tiers as a way to decide which model to pay for, not as a way to avoid paying.

Option 4: Use a cheaper model

The biggest single saving usually comes from not using a flagship model where you don't need one. Classification, extraction, routing, short summaries and simple rewrites rarely need the most capable model available, and budget models have become remarkably good at them.

Output tokens on GPT-6 Luna cost a twentieth of what they cost on Claude Opus 5.5. Even if a budget model only handles half of your traffic well, routing that half to it can cut the bill dramatically. The catch is quality: test a cheaper model on your own real inputs before switching, and keep the flagship for the requests that need it.

Option 5: A discounted API gateway

An API gateway gives you one API and one key for models from many providers, and some sell access below the official list price. The important difference from options 2 to 4 is that you don't change the model at all: you call the same Claude, GPT or Grok model you use today, through an API your code already speaks, and pay less per token.

Not every model is discounted: some budget models are sold at their list price because there's little margin left to share. Where discounts exist, they apply to input, output and cached tokens alike, so they compound with every other saving in this guide.

Combine the options

The cheapest setups don't pick one option; they stack them. A practical pattern that works for most products looks like this:

1. Prototype on free credits to find which models handle your task.
2. Route simple, high-volume requests to a budget model.
3. Keep a flagship model for the hard cases, and call it through a discounted gateway.
4. Turn on prompt caching for the parts of your prompts that repeat, which are billed at a fraction of the input price.
5. Put a spend limit on every API key so a bug or a runaway agent can't surprise you.

Each step is independent, so you can start with whichever is easiest. Moving your existing flagship traffic to a discounted gateway is usually the quickest win, because it requires no prompt changes and no quality testing: it's the same model.

Where GreenTokens fits

GreenTokens is built around options 4 and 5 together. One API key reaches Claude, GPT, Grok, DeepSeek, GLM, Kimi and more, including budget models, through OpenAI- and Anthropic-compatible endpoints, so switching is a base URL change. Every model shows its official price next to ours on the models page, you top up a prepaid balance with no subscription, and every key can carry daily and monthly spend limits.

Whichever route you choose, measure cost per task rather than cost per token, and re-check your choices every few months. Model prices move quickly, and the cheapest good-enough option today is rarely the same one a year from now.