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Repair common Windows errors and clear accumulated junk for a smoother, more stable PC - no reinstall needed.Free scan · no reinstallYou can build a useful Wordle solver by counting how often each letter appears in each position among the remaining possible answers, then scoring guesses against those counts. After every guess, simulate the game’s green, yellow, and gray feedback and discard any answer that would not produce the exact same pattern. This is a clear, inexpensive teaching heuristic—not a guarantee of optimal play.
What the solver needs to track
Wordle uses five-letter guesses and provides five color results per guess; the cited solver models a six-guess limit. A practical implementation needs a set of possible answers and a way to compare each one with the feedback received.
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It helps to keep two word pools distinct:
- answers: the words still possible as the hidden answer.
- allowedGuesses: words accepted as guesses, including words that are not possible answers.
A guess from the second pool can be useful even when it cannot be the answer: it may distinguish among several remaining candidates. Josh Stephenson’s repository documents a historical set of 2,315 answers and 10,637 guess-only words; those are snapshots, not current official counts. The repository documentation also describes filtering candidates from feedback.
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Score guesses using positional letter frequency
General English letter frequency is a blunt guide. A solver can make a more relevant estimate by counting letters at each position among the answers that remain possible. For example, if many candidates begin with S, that should affect the value of an S in the first position—not an S elsewhere.
#1 Best Overall
- Start with the selected answer list as the candidate set.
- For each of the five positions, count how many remaining candidates contain each letter at that position.
- Score a word by adding the count for its letter at each position.
- Sort the candidate guesses by score, highest first.
Because every candidate has five letters, dividing each positional count by the number of candidates would scale every score equally; it would not change the ranking. The cited example using an original answer list reports S as most frequent in position one, A in positions two and three, and E in positions four and five. These are properties of that particular list, not universal Wordle constants.
function positionalFrequencies(candidates) {
const counts = Array.from({ length: 5 }, () => Object.create(null));
for (const word of candidates) {
for (let i = 0; i < 5; i++) {
const letter = word[i];
counts[i][letter] = (counts[i][letter] || 0) + 1;
}
}
return counts;
}
function scoreWord(word, counts) {
return [...word].reduce((score, letter, i) =>
score + (counts[i][letter] || 0), 0);
}
function rankCandidates(candidates) {
const counts = positionalFrequencies(candidates);
return [...candidates].sort((a, b) =>
scoreWord(b, counts) - scoreWord(a, counts));
}
Here the guess pool and candidate pool are the same, so the highest-ranked guess is necessarily a remaining answer. If you want to consider all accepted guesses, score each word in allowedGuesses against frequencies calculated from answers; do not accidentally treat an allowed guess as a possible answer.
Rank #2
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Simulate feedback correctly, including repeated letters
Feedback must be exact before it is safe to prune candidates. For a guess and a possible answer, generate five results: green for an exact-position match, yellow for a present letter in another position, and gray for a guess letter with no remaining matching occurrence.
Repeated letters make a one-pass check unreliable. First mark greens and consume those answer-letter occurrences. Then, for each unmatched guess letter, mark yellow only if an unconsumed copy remains. Otherwise mark it gray. This prevents a guess with two copies of a letter from receiving two positive marks when the answer contains only one available copy.
Rank #3
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function feedback(guess, answer) {
const result = Array(5).fill("gray");
const remaining = answer.split("");
// Exact matches consume answer occurrences first.
for (let i = 0; i < 5; i++) {
if (guess[i] === answer[i]) {
result[i] = "green";
remaining[i] = null;
}
}
// Misplaced matches can use only occurrences not already consumed.
for (let i = 0; i < 5; i++) {
if (result[i] === "green") continue;
const matchAt = remaining.indexOf(guess[i]);
if (matchAt !== -1) {
result[i] = "yellow";
remaining[matchAt] = null;
}
}
return result;
}
function filterAnswers(answers, guess, observed) {
return answers.filter(answer =>
feedback(guess, answer).every((color, i) => color === observed[i])
);
}
Represent the observed pattern in the same order and with the same color labels as feedback. After each turn, replace the candidate set with the filtered result, recalculate positional frequencies, and rank again. The two-pass repeated-letter method is also described in the wordlesmith implementation documentation.
Use the loop after each Wordle turn
- Choose the answer list and allowed-guess list your program will use.
- Rank guesses using frequencies from the current answer candidates.
- Enter a guess in Wordle and record the five colors in order.
- Filter the answer candidates by retaining only words whose simulated feedback exactly matches the recorded colors.
- Recalculate positional frequencies from the reduced set and rank the next guess.
If filtering produces no candidates, check the recorded color order, the word-list coverage, and repeated-letter handling before changing the scoring formula. A candidate list cannot recover an answer that is missing from its underlying word list.
Rank #4
Frequency versus information-focused strategies
Frequency is transparent and cheap to compute, which makes it a strong baseline for learning. It is not the same objective as information gain: a high-scoring likely answer may be less useful than a guess that divides the candidates into more distinct feedback groups.
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- Entropy: favors guesses expected to reduce uncertainty.
- Expected remaining set size: favors guesses with smaller average feedback groups.
- Minimax: favors guesses that minimize the largest possible remaining group.
These objectives can choose a word that is not itself a plausible answer, provided it is an allowed guess. Such a probe can clarify the set, but it may use a turn that a lucky direct answer would not need.
The wordlesmith repository reports the following average-guess results on its original 2,315-answer set when guesses are restricted to remaining answers. They are results for that project’s data and benchmark assumptions, not a universal ranking or a promise for a current game:
| Strategy | Reported average guesses | Guess pool and answer set |
|---|---|---|
| Frequency | 3.640 | Remaining answers; original 2,315-answer set |
| Entropy | 3.598 | Remaining answers; original 2,315-answer set |
| Minimax | 3.677 | Remaining answers; original 2,315-answer set |
The same repository documents a broader 14,855-word valid-guess snapshot, identified as mid-2025, and notes that performance differs when the full pool is available. Its benchmark and strategy documentation distinguishes those pools and objectives. A separate 2022 paper by Chao-Lin Liu reports a baseline average of 4.078 guesses with 1.77% of games exceeding six guesses, and a best-performing strategy averaging 3.674 guesses with 0.65% exceeding six, on the paper’s stated simulator and 2,315-word set. These results are not directly comparable to the repository figures because solver methods and benchmark procedures differ. See Liu’s paper.
Word lists and benchmark limits
Word lists change, and a historical answer list can omit a word that is accepted or used in a later version of the game. The wordlesmith project specifically cautions that its original answer set may miss legitimate answers and identifies its broader valid-word snapshot as mid-2025. Neither that snapshot nor another repository list should be described as the current official New York Times list.
When reporting a solver benchmark, specify the answer set, whether guesses are limited to remaining answers or may come from all valid guesses, and how outcomes beyond six turns are counted. Average guesses alone can conceal worst-case behavior and failures. For a teaching project, the most valuable early correctness check is usually the feedback simulator: an incorrect duplicate-letter result corrupts every later candidate set, regardless of how sophisticated the ranking is.
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