How many rs are in strawberry
Executive summary
The simple, verifiable answer is: the word "strawberry" contains three instances of the letter "r" (positions 3, 8 and 9 when counted left to right) [1] [2]. The recent media fuss and technical write‑ups are not about English orthography but about how large language models sometimes miscount those letters because of tokenization and heuristic shortcuts in their internal representations [3] [4] [5].
1. The plain fact: count the characters — three R’s
A literal letter‑by‑letter reading of "strawberry" shows three occurrences of the letter "r," and multiple educational and coding examples demonstrate that straightforward character counting yields the answer "3" [1] [2]; this is the objective, human‑readable truth about the string of letters s‑t‑r‑a‑w‑b‑e‑r‑r‑y [2].
2. Why the headline exists: AI models saying “two”
Major reports documented that several prominent LLMs produced the wrong answer — commonly "two" — when asked how many "r"s are in "strawberry," and journalists used the error as a vivid example of AI brittleness (Inc. observed models answering "two" and TechCrunch summarized similar failures) [3] [4].
3. What’s going wrong under the hood: tokenization and heuristics
Technical discussions and analyses point to subword tokenization as the core cause: models frequently break "strawberry" into chunks like "straw" + "berry" or other token combinations, which can cause a model to count tokens containing an "r" rather than individual characters, or to conflate a doubled middle "rr" as a single group [2] [5] [6].
4. Community forensics: bugs, forum reports and reproducibility
Developers and users reproduced the miscount in community threads and bug reports, showing consistent model behavior that yields the wrong count and even insisting on the wrong answer despite subsequent prompts — threads on OpenAI’s community forum and Hacker News captured step‑by‑step reproductions and token ID evidence [7] [8].
5. Broader implications: a symbol of LLM limits, not a linguistic mystery
Commentators used the "strawberry R" failure as shorthand for a broader class of LLM weaknesses — tasks requiring precise, character‑level manipulation or reliably deterministic operations — and analysts argued this reflects model architecture and training data patterns, not a failure of English spelling [5] [6] [9].
6. Fixes, workarounds and what the sources report about solutions
Practitioners and write‑ups propose clear workarounds such as prompting the model to treat characters individually, using external deterministic code to count characters, or improving tokenization/architectural handling of character‑level tasks; these remedies illustrate that the problem is engineering‑addressable rather than a linguistic paradox [2] [10] [5].
7. Bottom line and what to take away from the noise
The empirically correct answer — three "r"s — is simple and verifiable by direct inspection or a one‑line program, and the media attention largely documents an instructive AI failure mode: the narrative is a cautionary tale about trusting LLM outputs for exact, low‑level tasks unless the model is prompted or instrumented to operate at the character level [1] [3] [5].