AI API pricing compared: what you'll really pay in 2026

The per-million-token numbers on pricing pages are the least useful way to compare AI APIs, because your bill is shaped by things those numbers hide. Here’s how to reason about real cost before you commit a product to a provider.

Input and output tokens are priced differently

Almost every provider charges more for output tokens than input tokens, often several times more. That changes your optimisation depending on the workload:

• Summarisation / extraction (huge input, small output) is dominated by input price.
• Generation / drafting (small input, huge output) is dominated by output price.

Compare providers using your input:output ratio, not a blended headline rate.

Caching is the discount most teams miss

If you send the same large context repeatedly — a system prompt, a knowledge base, a long document — prompt caching can cut the cost of those repeated tokens dramatically. For chat products and agents that resend context every turn, this is often the difference between viable and not. Check each provider’s caching model and TTL; it can matter more than the base rate.

Batch and async tiers

For work that doesn’t need an instant answer — bulk classification, offline enrichment, evals — many providers offer a batch tier at a meaningful discount. Moving non-interactive workloads there is free money.

Tiers within a provider

Every major provider sells a small/fast model, a mid model, and a heavy reasoning model at very different prices. The expensive model is rarely the right default. A common winning pattern: route easy requests to the cheap model and escalate only the hard ones to the expensive one. Model routing can cut a bill by more than switching vendors.

The hidden costs

• Retries and failures still cost tokens. A flaky pipeline pays for work it throws away.
• Long context isn’t free. Stuffing a giant context every call is convenient and expensive; retrieve what you need instead of sending everything.
• Reasoning/thinking tokens on heavy models count toward output. A model that “thinks” more is more capable and costs more per answer.

How to actually compare

1. Estimate your real input:output ratio and monthly volume.
2. Price the specific model tier you’d use, not the flagship.
3. Factor in caching for repeated context and batch pricing for async work.
4. Run a small live test and read the actual invoice — projected and real bills diverge more than anyone expects.

The takeaway for 2026: sticker price rarely predicts your bill. Caching, batching, and model routing move it far more than the headline per-token rate. Design for those three and you can often cut spend by half without changing what your users see.