How much does it cost to use AI API in 2026: price comparison (OpenAI, Claude, and Gemini)

Choosing between OpenAI, Anthropic, and Google for your AI application is no longer just about capability. All three offer excellent models, and the cost difference between them, and between each provider's models, can be more than a hundredfold. Knowing how to compare prices is what separates a profitable product from an out-of-control budget.

This article compares the prices of the main AI APIs, with current values as of June 2026. As these prices change frequently, always check the official page of each provider before making a final decision.

How to read the prices

All providers charge per million tokens, with separate values for input (what you send) and output (what the model generates). The usual notation is input / output. A value of $3 / $15 means three dollars per million input tokens and fifteen per million output tokens.

Remember that output almost always costs five times more than input, and that one million tokens is equivalent to about 750,000 words.

Comparison table (June 2026)

Values in dollars per million tokens, input / output, before caching or batch discounts.

What this table shows

At the high end, prices have converged. Anthropic's and OpenAI's most capable models are in the same ballpark for input, with OpenAI slightly more expensive for output. Gemini 3.1 Pro is below both in this category.

In economical models, Google leads. Gemini 2.5 Flash-Lite, at $0.10 / $0.40, is one of the cheapest models on the market, often several times cheaper than equivalent options from competitors. For simple, high-volume tasks, it's hard to beat on price.

The biggest factor is not the provider, it's the model. Notice that within OpenAI itself, the difference between GPT-5.5 and GPT-5.4 Nano is enormous. Choosing the right model within a provider has more impact on cost than choosing between providers.

The discounts that change everything

List prices are just the starting point. All three providers offer mechanisms that drastically reduce effective cost:

• Batch: asynchronous processing with 50 percent discount, across all providers.
• Prompt caching: reuse of repeated context with up to 90 percent savings on reads.

By combining both, it's possible to reduce the cost of suitable workloads by more than 90 percent compared to the list price.

How to choose

The right question is not which is the cheapest overall, but which is the cheapest for your task, at the quality you need. A practical approach:

1. Define the task and the minimum acceptable quality. Not everything needs the top-tier model.
2. Test economical models first. Often a cheap model solves simple tasks like classification, extraction, or support responses.
3. Reserve high-end models for complex reasoning, difficult code, or high-value work.
4. Apply caching and batch where the usage pattern allows.

Conclusion

In 2026, price is no longer a barrier to using quality AI. Token costs have fallen sharply, and there are options for all budgets. The smart decision is not to commit to one provider, but to choose the right model for each task and apply the available savings mechanisms.

As values change frequently, treat this table as a snapshot from June 2026 and always confirm official prices before finalizing an architecture.