Google released a new kind of AI service on Wednesday, and it writes nothing at all. The Gemini Decisions API(opens in a new tab) takes one input, such as an email, a product photo, a contract PDF or a call recording, and answers up to 1,000 closed questions about it in a single pass: yes or no, pick one from a list, or score it on a scale. Each answer comes back as a probability with a confidence score from 0 to 1, in 150 to 250 milliseconds, Google says. An API is a service that a program calls directly, not a chat window.
The price is the news. Google charges $0.10 per million input tokens and nothing for output, because there is none. A token is a small chunk of text, roughly three quarters of a word. The shared input is billed once however many questions you ask, so checking a 1,000-token email costs about a hundredth of a cent, plus the words of the questions themselves. Google says(opens in a new tab) that is nearly five times cheaper than its own Gemini 3.5 Flash-Lite model across its benchmark suite, and that Gemini Notebook cut waiting time on everyday queries by up to 40% by using the API to pick which model answers. Both are Google's own figures, and the post shows its accuracy results only in charts, with no numbers in the text.
Here is why that matters for spending. Ramp's AI Index, built from payments at more than 85,000 US companies, shows the average price per million text tokens fell 21% last month, yet 69.5% of tracked spending still went to the most expensive models, The Decoder reported(opens in a new tab). A lot of what businesses pay those models to do is sorting: is this ticket a refund request, does this photo show damage, does this invoice match the order. Customer support rose from 0.8% of tracked AI spending in June to 13.3% in October, and Ramp's chief economist, Ara Kharazian, notes that such work tends to favor cheaper, faster models.
The confidence score is both the useful part and the risky part. It lets a program act on clear cases and pass unclear ones to a person. But a score is only as good as its calibration on your data, and Google's calibration claim rests on its own benchmark suite. The documentation(opens in a new tab) lists the API under v1beta, caps yes or no probabilities between 0.01 and 0.99, and says context caching and batch pricing are not supported yet. Google's post lists support in Google Cloud as coming soon, which matters if your company buys AI through a cloud contract.
If you or your team pay a chat model to sort things, try this before you switch. Take 200 items you have already sorted by hand: support tickets, leads, receipts, listings. Run them through the free prototyping tier in Google AI Studio with the question you use now, and find the confidence level above which its answers match yours almost every time. Automate only above that line and send the rest to a person. The share that falls below the line, not the token price, decides what you save. Leave customer personal data out of the test until you have read the data terms for the tier you use.
Action items
Google released a new kind of AI service on Wednesday, and it writes nothing at all. The Gemini Decisions API takes one input, such as an email, a product photo, a contract PDF or a call recording, and answers up to 1,000 closed questions about it in a single pass: yes or no, pick one from a list, or score it on a scale. Each answer comes back as a probability with a confidence score from 0 to 1, in 150 to 250 milliseconds, Google says. An API is a service that a program calls directly, not a chat window.
The price is the news. Google charges $0.10 per million input tokens and nothing for output, because there is none. A token is a small chunk of text, roughly three quarters of a word. The shared input is billed once however many questions you ask, so checking a 1,000-token email costs about a hundredth of a cent, plus the words of the questions themselves. Google says that is nearly five times cheaper than its own Gemini 3.5 Flash-Lite model across its benchmark suite, and that Gemini Notebook cut waiting time on everyday queries by up to 40% by using the API to pick which model answers. Both are Google's own figures, and the post shows its accuracy results only in charts, with no numbers in the text.
Here is why that matters for spending. Ramp's AI Index, built from payments at more than 85,000 US companies, shows the average price per million text tokens fell 21% last month, yet 69.5% of tracked spending still went to the most expensive models, The Decoder reported. A lot of what businesses pay those models to do is sorting: is this ticket a refund request, does this photo show damage, does this invoice match the order. Customer support rose from 0.8% of tracked AI spending in June to 13.3% in October, and Ramp's chief economist, Ara Kharazian, notes that such work tends to favor cheaper, faster models.
The confidence score is both the useful part and the risky part. It lets a program act on clear cases and pass unclear ones to a person. But a score is only as good as its calibration on your data, and Google's calibration claim rests on its own benchmark suite. The documentation lists the API under v1beta, caps yes or no probabilities between 0.01 and 0.99, and says context caching and batch pricing are not supported yet. Google's post lists support in Google Cloud as coming soon, which matters if your company buys AI through a cloud contract.
If you or your team pay a chat model to sort things, try this before you switch. Take 200 items you have already sorted by hand: support tickets, leads, receipts, listings. Run them through the free prototyping tier in Google AI Studio with the question you use now, and find the confidence level above which its answers match yours almost every time. Automate only above that line and send the rest to a person. The share that falls below the line, not the token price, decides what you save. Leave customer personal data out of the test until you have read the data terms for the tier you use.
Researched and drafted by an automated workflow, then reviewed and edited by a human editor before publication. Every source is linked. See how we use AI here.
Also worth knowing
- GoogleGemini Decisions API: fast, calibrated multimodal decisions(opens in a new tab)
Up to 1,000 yes or no, multiple choice or rubric questions per input, across text, images, audio, video and PDFs. $0.10 per million input tokens, no output charge, and a free prototyping tier in AI Studio. Accuracy appears only in charts.
- The DecoderUS corporate AI spending hits a new high in October, but growth slows to 1.65 percent(opens in a new tab)
In Ramp's payment data from 85,000+ US companies, the top 1% spend a median $10,200 per employee a month and supply 80% of OpenAI and Anthropic revenue. Most AI case studies come from that 1%; check whether your company looks like them.
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AI agents wrote over six million lines of machine-checked proof, in four weeks, that the core code behind Signal's encryption keeps its security promises. The work covered that core cryptographic code, not the whole app, and Google has announced no product built on it.
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A researcher's test agent pulled customer records from an old internal WorkOS sales app whose API was open to the internet, accepted a predictable development password and had no rate limit. Leftover test passwords on old apps are what these tools find.
- ElevenLabsIntroducing Scribe v3 and Scribe v3 Live(opens in a new tab)
ElevenLabs reports about 27% fewer word errors than v2 across 102 languages on its own test, plus live speaker labels for the first time. If you transcribe interviews or calls, rerun your hardest recording before paying for cleanup.