Google is making artificial intelligence substantially cheaper for businesses to use, launching a new Gemini model Thursday at half the price of the model it is replacing as the competition to automate everyday business work intensifies.
The new Gemini 3.7 Flash is aimed at software coding, AI agents and automated business workflows. Google is offering introductory pricing through the end of 2026 of 75 cents per 1 million input tokens and $3.75 per 1 million output tokens, compared with $1.50 and $7.50 for Gemini 3.6 Flash.
But what does that actually mean in dollars?
A token is a small piece of text processed by an AI model. Roughly speaking, 1 million tokens can represent around 750,000 English words, depending on the material.
That means a business could feed Gemini roughly 750,000 words of documents for about 75 cents.
A 10,000-word batch of invoices, contracts, reports or other documents would cost roughly one penny for the AI to read and process on the input side.
The output costs more. If Gemini generated the equivalent of 100,000 words in responses, summaries, reports or other work, the output portion would cost roughly 50 cents at the introductory price.
That is the real business story.
Companies pay AI providers based largely on how much information their applications send into a model and how much the model generates back. Cutting those prices in half can transform the economics of using AI hundreds, thousands or even millions of times.
A company might use the model to review invoices, summarize contracts, categorize customer emails, prepare reports, analyze documents, write software or operate customer-service systems.
One AI-assisted email may save only a few minutes. But a system processing 100,000 documents or customer requests can potentially eliminate hundreds or thousands of hours of repetitive work.
That is why the AI competition is increasingly becoming about something business owners understand very well: cost per job.
The industry spent the past several years competing over which company could build the smartest AI model. Increasingly, Google and its rivals are competing over how inexpensively those models can perform useful work.
For businesses, that distinction matters enormously.
An AI system that saves an employee five minutes but costs several dollars every time it runs may not make economic sense. If that same job costs pennies, the calculation changes.
Google is specifically positioning Gemini 3.7 Flash for agentic workflows, where AI does more than answer a single question. An AI agent can potentially receive an assignment, examine documents, interact with software, make decisions and complete multiple steps before returning the finished result.
Imagine an accounts-payable department receiving hundreds of invoices.
Instead of an employee opening each invoice, identifying the vendor, reading the amount, entering the information into another system and flagging discrepancies, an AI agent could potentially perform much of that workflow automatically — with employees reviewing exceptions rather than every transaction.
The same economics can apply to insurance documents, purchase orders, customer-service tickets, legal paperwork, inventory records and software development.
For small and midsize businesses, falling AI prices may be especially important.
Large corporations can afford multimillion-dollar experiments even when the return is uncertain. Smaller companies generally need a much clearer payoff before changing their operations.
At 75 cents per million input tokens, however, the cost of having AI read enormous quantities of text is becoming almost negligible compared with the cost of the employee time traditionally required to process it.
Google also has a strategic reason to push prices lower. It is battling OpenAI and Anthropic for enterprise customers, and price is becoming an increasingly important part of that competition.
Gemini 3.7 Flash therefore represents something larger than another AI product release.
The price of intelligence itself is falling.
And as that happens, the question facing business owners changes from “Can we afford AI?” to “Which jobs are we still paying people to do manually that technology can now perform for pennies?”
That may ultimately prove far more disruptive than whichever company wins the next AI benchmark.
JBizNews Desk | Mountain View, Calif.
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