A Wordle AI guesser reverses the normal roles: you choose the secret five-letter word and the bot makes the guesses. This tool calculates every green, yellow, and gray tile locally, filters a 219-word answer model, and selects the next guess by comparing how well each playable word separates the remaining candidates. It shows the evidence behind every move instead of claiming to be an official or universally optimal Wordle bot.
What this Wordle AI guesser actually does
The tool turns a familiar solving task into a small challenge against the computer. Enter one word from the site's local 219-answer model, keep it secret from anyone beside you, and let the browser produce the bot's first guess. Because the target is already stored locally, the page can score the row automatically. You never have to tap five tiles or remember how an extra copy of a letter should be colored. After each row, the interface reports the actual candidate count, the expected candidate count before feedback, and the information gained by the chosen guess.
This is a deterministic word-game model, not a language model pretending to reason. It compares the same 9,983 accepted five-letter guesses exposed by this site's word tools, partitions the remaining targets by their possible feedback patterns, and prefers the word with the smallest expected surviving group. The result is reproducible for a given candidate state. A different dictionary, answer weighting, hard-mode rule, or tie-breaker can choose a different word, so the page names its model and never presents one run as a universal ranking.
You choose the target
Use a local model word or ask the tool to select a random five-letter challenge.
The browser scores every tile
Green matches are assigned first, then yellow copies, so repeated letters stay consistent.
Every move has a reason
Candidate counts and information gain show what the guess changed in this model.
Challenge the bot without manually grading colors
Begin by typing exactly five English letters. The input accepts only words in the 219-target model because the solver must be able to prove when it has found the secret. If your word belongs to the broader 9,983-guess bank but not the target set, the page explains the difference instead of silently running a model that can never win. The random button is the fastest way to start when you want a fair target without browsing the answer model.
Once the challenge starts, the secret remains inside the current browser session. Press the next-guess button to let the model evaluate the live candidate set. The colored row appears together with its measured effect, and the next turn uses only targets that would have produced exactly that complete feedback pattern. Continue until the guess equals the secret or six rows have been used. At the end, copy a spoiler-light grid, compare the run with your local history, or reset with a different word.
Lock a secret
Enter a supported target or generate one randomly; no account or upload is required.
Advance one guess
Inspect the row and its candidate reduction before asking the bot to continue.
Review and share
Use the finished grid and guess count without exposing the secret in the shared text.
How the solver chooses its next five-letter word
For every playable guess, the solver imagines the feedback that each remaining answer would return. Five tiles with three possible states create up to 243 theoretical color patterns, although most guesses produce far fewer patterns on a small candidate set. Answers that return the same complete pattern enter the same bucket. The solver adds the squared bucket sizes to measure expected remaining uncertainty, tracks the largest bucket as a worst-case check, and uses Shannon entropy to report the result in bits.
The selected guess minimizes expected survivors first and worst-case survivors second. When scores tie, the model prefers a word that could itself be the answer, then uses stable alphabetical ordering. This greedy policy looks only one move ahead. It is intentionally lighter than exact dynamic programming and can run entirely in the page on a phone. When only one candidate remains, the bot submits that word directly rather than spending another turn on a probe.
Published exact Wordle model versus this browser bot
The research model and this page both evaluate feedback partitions, but their datasets and optimization depth are intentionally different.
| Measure | Published exact-solution research | This Wordle AI guesser |
|---|---|---|
| Possible targets | 2,315 original solutions | 219 local candidate answers |
| Playable guesses | 12,972 original guesses | 9,983 local accepted guesses |
| Selection method | Exact dynamic programming across future states | Greedy expected reduction plus worst-case tie-break |
| Reported result | 3.421 expected guesses from SALET in the paper's model | Per-secret rows, candidates, and information gain |
What the measurements can support
Bertsimas and Paskov's exact-solution study formalized Wordle with 2,315 possible solutions and 12,972 allowable guesses, then reported an average of 3.421 guesses for its optimal normal-mode policy starting with SALET. The same study says its exact policy finds every target in at most five guesses in that model. Those are published research results, not values copied into this tool's score.
This page performs a smaller reproducible measurement against the current Dont Wordle data: 219 equally weighted targets, 9,983 playable guesses, five tile positions, three feedback states per tile, and a maximum of six displayed rows. The model date is July 23, 2026. Every dynamic number beside a row is recalculated from the candidates that remained immediately before that guess.
- Expected remaining is the probability-weighted average bucket size before the secret's feedback is applied.
- Actual remaining counts the targets that match the full five-tile pattern returned by your secret.
- Information gain is the base-two reduction in uncertainty for the observed branch and is shown in bits.
Why uncertainty is the useful unit
Shannon framed communication as recovering a selected message from a set of possibilities. A Wordle row is a tiny version of that problem: its color pattern tells the solver which possible target remains consistent.
“The fundamental problem of communication is that of reproducing at one point either exactly or approximately a message selected at another point.”
Claude E. Shannon, A Mathematical Theory of Communication (1948)
Original English quotation, kept under 25 words; the surrounding explanation is this site's application to word-game feedback.
A fair way to test the bot
Use the same procedure for several targets so one lucky or unlucky branch does not decide your opinion of the model.
- Choose a supported secret without inspecting the model's next guess.
- Record the actual candidate count after each colored row.
- Compare a common word, a repeated-letter word, and a random target.
- Judge only runs made with the same model date and rules.
Sources and reproducible model notes
The external links below document the published exact solver and the information-theory foundation. Local counts come directly from the current source arrays used by this page.
Wordle AI guesser FAQ
Is this the official WordleBot?
No. It is an independent browser tool on Dont Wordle. It uses a disclosed local answer set, accepted-guess list, duplicate-letter scorer, and greedy information-gain policy.
Does the bot use a large language model?
No. It deterministically compares feedback partitions for playable five-letter words. The AI label describes the computer opponent experience, while the page explains the actual algorithm.
Why can I not enter every accepted guess as the secret?
The broad bank contains 9,983 playable guesses, but only 219 words are targets in this model. Restricting the secret to that target set prevents an unwinnable challenge.
How does the tool handle repeated letters?
It assigns exact-position greens first, then uses each remaining target copy for yellow tiles from left to right. Extra unmatched copies become gray.
Is the highest information-gain word always the best guess?
Not universally. This bot minimizes expected survivors, then worst-case survivors, with stable tie-breaks. Exact policies, weighted answers, hard mode, and other dictionaries can prefer another move.