Google unveils Gemini 1.5 Pro with one million tokens

Google DeepMind today unveiled Gemini 1.5 Pro, a new version of its artificial intelligence model family that can handle up to one million context tokens in testing. That is a significant capability because it dramatically expands the amount of information the model can keep in view during a single conversation or task.

In practical terms, Google estimates that the limit is equivalent to around 700,000 words, 11 hours of audio, one hour of video or tens of thousands of lines of code. The model does not just process text: Gemini 1.5 Pro retains Gemini’s multimodal capabilities and can combine text, images, audio and video in a single prompt.

A context window more than 30 times larger than Gemini 1.0

A model’s context window is its temporary workspace. It determines how much material the model can read and connect before responding. It should not be confused with permanent memory: processing a book during one query does not mean the system will remember it in future conversations.

Gemini 1.0 Pro, introduced by Google in December, offered a context window of around 32,000 tokens. Gemini 1.5 Pro raises that figure to one million—more than 30 times the previous limit. Google DeepMind is also testing contexts of up to 10 million tokens in research, although that capability is not part of the preview announced for developers and businesses.

Until now, models with large context windows had shown that they could accept extremely long documents, but not always that they could accurately use information located at the beginning or in the middle of that material. The challenge is not simply storing more text, but retrieving the relevant detail and reasoning about it without getting lost among thousands of pages.

Google says Gemini 1.5 Pro achieves near-perfect retrieval in its tests of finding information within large volumes of content, including combinations of text, audio and video. These are evaluations developed by the company itself, so its performance in real-world tasks and with less structured information remains to be tested.

Mixture of experts: activating only part of the model

The other technical innovation is its mixture-of-experts architecture, known as MoE. Rather than activating every internal component of the model for each prompt, a routing system directs each part of the task to the most suitable “experts.”

This approach makes it possible to increase the model’s overall capacity without requiring every response to use all of its parameters. Google has used the technique in previous research, and other companies deploy it in some of their models, but Gemini 1.5 Pro brings it to one of the company’s flagship products.

Google says the model delivers performance comparable to Gemini 1.0 Ultra, its most powerful version to date, even though Gemini 1.5 Pro is described as a mid-sized model. The company has not disclosed the number of parameters in this version, a figure that by itself also cannot reliably measure a model’s usefulness.

From contract analysis to software maintenance

One million tokens opens up clear possibilities for people working with large amounts of information. A law firm could ask the model to compare a contract with hundreds of related documents. A company could analyze a complete meeting transcript, internal manuals and selected emails without splitting them into chunks. A programming team could provide the system with a large codebase to identify dependencies or explain the impact of a change.

There are audiovisual applications as well. A model given an hour of video could answer questions about a specific sequence, locate an element that appears briefly or connect an oral explanation with a slide shown later.

But a long context does not remove the usual limitations of generative AI. The model may misinterpret an instruction, make up a plausible answer or overlook an important exception. In legal, financial or medical settings, its output will still require human review and controlled access to data.

Initially available to a limited group

Gemini 1.5 Pro is initially arriving in private preview for developers and enterprise customers through AI Studio and Vertex AI, Google’s platforms for building and deploying AI applications. The company has opened a waitlist for those who want to try the one-million-token context window.

The announcement puts context size among the main areas of competition between leading AI labs. Over the past year, the race has focused on getting models to write, code and reason better; now, how much material they can handle at once matters too. The decisive test will be whether that scale translates into reliable, fast and cost-effective responses for businesses that want to incorporate it into their tools.