Google has introduced a new feature called "implicit caching" in its Gemini API, designed to make using its latest AI models—Gemini 2.5 Pro and 2.5 Flash—more affordable for developers.

The company claims this feature can reduce costs by up to 75% when handling repeated content (also known as “repetitive context”) sent to its models.

What is Implicit Caching?

In the world of AI, caching is a common technique that saves frequently used data so it doesn't need to be processed again, saving both time and money. For instance, if users often ask the same questions, the model can reuse cached answers instead of generating them from scratch each time.

Previously, Google only supported “explicit caching,” which required developers to manually specify the most frequently used prompts. While it offered cost savings, it also involved extra work—and many developers weren’t happy with how it performed. Some even reported unexpectedly high API bills.

To address this, Google is rolling out “implicit caching,” which works automatically—no manual setup required. It’s turned on by default in the Gemini 2.5 models. If a request is similar to a past request (especially at the beginning of the prompt), the system will use cached data and pass on the savings.

How does it work?

Implicit caching is supported in Gemini 2.5 Pro and 2.5 Flash models.

It activates when a request starts with a similar “prefix” to a previous one.

Minimum token count for caching.

• 1,024 tokens for Gemini 2.5 Flash
• 2,048 tokens for Gemini 2.5 Pro

To improve cache usage, put repeated content at the beginning of your prompt and place changing content at the end.

Tokens are the building blocks AI models work with—1,000 tokens is roughly 750 words.

A Few Caveats

Although implicit caching promises easier and automatic cost savings, developers should approach it with a bit of caution. Google has not yet provided independent verification of the claimed savings so real-world results may vary. The effectiveness of caching depends largely on how consistently prompts are structured—placing repeated content at the beginning and variable details toward the end will help maximize savings. Developer feedback in the coming weeks will provide a clearer picture of how well this new system performs.

Still, with AI model usage growing rapidly, this update could offer meaningful cost relief for teams building on Google’s platform.