Cipher by SurbeeEarly access

Cipher

Join for early access and get the Cipher playground: paste any response and watch every check run 👀

CIPHER.OS1
verdicts.log
  1. botwebsite visit, mouse tracked0.64flagged
  2. botwebsite visit, mouse tracked0.76flagged
  3. carefulpersonality survey, 60 ratings0.61false alarm
  4. personclicking through a page0.15clear
  5. own wordsrestaurant review, typed0.15clear
  6. personwebsite visit, mouse tracked0.15clear
  7. personwebsite visit, mouse tracked0.15clear

real responses Cipher never trained on. left: what it really was. right: what Cipher said.

Clock Tool 1.1
 
 
stopwatch
103µs

one full response, every in-house check

BOTS
BOT

scripts posing as people

+mouse+timing
IER
IER

people rushing through

+speed+patterns
COPY
COPY

answers typed out from somewhere else

+rhythm+pauses
AI
AI

answers written by a chatbot

+words+paste
last 500 mouse moves

black: a person. magenta: a humanlike bot.

Snake 1.1

click to play

We graded Cipher on someone else’s exam

Not our data

Real bots, people told to answer carelessly, people retyping essays and real posts, all collected and labelled by other researchers. For AI we added answers we generated from today’s chatbots, since no public dataset has them yet. Cipher never saw the answer key 🙈

Tuned, then held out

Cipher was tuned on one slice of each dataset and scored on slices it had never seen. Every response ran through the highest tier with every check on, the same path a live survey takes.

93%

of bots Cipher had never seen were flagged, and 0 of 260 real people

0.94

score for spotting careless respondents, where 1.00 is perfect. Level with the best published method

4 in 10

copy-typists caught at a 3% false-alarm rate

0.4%

of 499 real people’s posts wrongly called AI

Run it yourselfpnpm cipher:public-benchOne command downloads every dataset and reruns every benchmark on this page.

Help Cipher get better

Real answers, real tricks

Cipher learns from examples of people answering honestly and people trying to fool it. Take a four minute survey, pick a side, and your answers go straight into training the next version of Cipher.

Anonymous

No name, no email, no account. We look at how you answer, like timing and typing rhythm, and use it only to make Cipher better.

Take the survey (opens in a new tab)

103µs a response

Cipher
103 µs per response, ten questions, every in-house check
A blink
150 ms, for scale 😌
Text model
329 ms per written answer, network included

*Median of 2,000 runs on an Apple M4, one core. Balls slowed down so you can see them.

Cipher

responses Cipher scored while you watched0

A Blink

Free until you want the AI checks

Cipher.Cost

PRICE PER RESPONSE, BY TIER

Basic · 9 checks · every plan

Tier 1Free

Standard · 18 checks · every plan

Tier 2Free

Enhanced · 27 checks · Pro and Max

Tier 30.2¢

Advanced · 35 checks · Max

Tier 41¢

Maximum · 45 checks · Max

Tier 55¢

FreeTiers 1 and 2, on every plan. 18 checks and no AI calls.

$50Per 1,000 responses with all 45 checks on. One credit is one cent.

Built for review

Every response gets a score and its reasons

Risk runs from 0 to 1. Below 0.4 a response is kept, from 0.4 it waits for a person, from 0.6 it is flagged. Owners move both lines, and each check reports what it saw.

Grid: real verdicts on responses Cipher never trained on. Green kept, pink review, magenta flagged, white wrong.

Cipher.Confidence

CIPHER RISK SCORE

45 / 48 RIGHT

Fewer false accusations than the detectors it was measured against

False.Accusations

REAL PEOPLE WRONGLY CALLED AI

Reddit posts

Binoculars7.2%
GPTZero3.0%
Originality2.0%
Cipher0.0%

Reviews

Binoculars5.7%
GPTZero5.0%
Originality4.0%
Cipher0.8%
Fewest false accusations 😇Real Reddit posts and reviews written by people. The other detectors’ numbers come from a published benchmark at their default settings.
Held-Out Scoreboard

CIPHER VS PUBLISHED METHODS, SAME DATA

People rushing the whole survey

Cipher0.94
Published0.94

People who started rushing partway

Cipher0.88
Published0.88

Bots, from how the mouse moves

Cipher93%
Published93%

People retyping text from elsewhere

Cipher0.81

No published detector works without per-person training.

Level with published methodsWhere researchers published a detector for the same data, Cipher matches or beats it on responses it never trained on.
Cipher Case Files

Four ways a fake gives itself away

45 checks run on every response at the highest tier. Here are four patterns up close, and 16 of the tells behind them below. The other 29 stay off this page, because bots can read too.

bots.case

Bots move like metronomes

A script glides the pointer at an even speed and fires events on a fixed clock. A hand speeds up, slows down and stops. Cipher flagged 93% of bots it had never seen, the humanlike ones included, without flagging a single person 🫡

careless.case

0.94AUC

Careless people leave a pattern

Faster than anyone reads, the same rating again and again, and out of step with how everyone else answers. Level with the best published method.

copying.case

Copying has a rhythm

People retyping text run on in long, even bursts and almost never correct. Cipher caught 45% of them on essays it never trained on.

ai-text.case

  • GPT-625%
  • GPT-5.550%
  • Claude Opus 5.544%
  • Claude Sonnet 5.553%
  • Grok 4.728%

Chatbot answers have to get in somehow

We wrote answers with today’s chatbots and scored the words alone. Cipher caught 40% of plain answers and 0% of answers told to sound human, while wrongly flagging 0.4% of real people. Words are not enough, so Cipher also watches how an answer arrives: pasted in whole, or typed out at a copy-typist’s steady pace.

Some of the tells

wobble testclockworknever lets gopuppet stringsspeed readerstraight lineodd one outyes to oppositestyping rhythmspotless draftpaste trailinvisible inkassistant habitscontradictionsshared answerssame room

And then there’s and two redacted techniques. We can’t tell you about those 🤐

Seven public research datasets with known answers: careless-responding experiments, recorded web bots, people writing and retyping essays, and real posts next to text from eleven older AI models. Other researchers collected and labelled all of it. On top of that, we generated answers from today’s chatbots ourselves, because no public dataset has them yet.