AI Model Pricing in 2026: Real Cost-Per-Token for Power Users

Knowing what each AI model actually costs is the difference between a $5 month and a $500 one. This is the real per-token pricing for the major models in 2026, what a typical question costs on each, and how to keep a multi-model habit cheap. For when to actually pick each one, see Which AI Model for Which Task?.

How much does each AI model cost per token in 2026?

AI models are billed per token (roughly 4 characters), with separate rates for the tokens you send (input) and the tokens the model writes back (output). Prices below are in US dollars per one million tokens.

Local models run through Ollama cost nothing per token: you have already paid for the hardware, and nothing leaves your machine.

What does a typical query actually cost?

Per-million numbers are hard to feel. Here is the cost of one realistic query of about 2,000 input tokens (a paragraph of context plus a question) and 500 output tokens (a few paragraphs back):

The spread is enormous: the same question costs about $0.0004 on DeepSeek V3.2 and about $0.04 on o3, a 100x difference. For most everyday work you simply do not need the most expensive option.

Why is output more expensive than input?

Output tokens cost more because they are generated one at a time, which is the compute-heavy part. Across these models, output runs roughly 4 to 6 times the input rate. That means verbose answers cost real money: asking a model to "be concise" is a cost lever, not just a style choice. Many providers also offer a discounted rate for cached input (reused context), shown in the last column.

Which models give the best value?

It depends on the job, not on a single winner:

• Cheapest usable tier: local models (free), DeepSeek V3.2, Gemini 3 Flash, and the "mini" tiers handle summarizing, drafting, classification, and high-volume tasks for a fraction of a cent.
• Mid tier: Claude Sonnet, Gemini Pro, and GPT-4o balance quality and price for most real work.
• Frontier tier: Claude Opus 4.8, GPT-5.4, and o3 earn their higher price only on genuinely hard reasoning, code, or analysis.

The expensive models are not "better" at everything, just pricier. Picking one model and paying its rate for every task is the hidden cost of committing to a single AI.

Does querying several models at once cost a lot more?

No, not if you are sensible about it. Running the same prompt across three mid-tier or cheap models still lands in the low single-digit cents, often less than one call to a frontier model. A common pattern is to Compare a few cheap models first and only escalate the hard cases to a frontier model. That is the core idea behind querying multiple models at once: you buy a second and third opinion for the price of a rounding error.

How do you keep multi-model costs under control?

• Use your own provider keys or the ones we manage, and pick the models you want. With your own keys you pay each provider their published rate directly, exactly the numbers above.
• Managed credits: Pro and Power include a monthly pool of AI Credits; beyond the pool, usage is billed at provider cost plus a small service fee.
• Run locally: point aiDex at Ollama and the per-token cost is zero.
• Match the model to the task and keep prompts tight. The biggest savings come from not sending a frontier model work a cheap one would nail.