Ask ChatGPT for something random and you may get a confident answer that looks arbitrary. That is not the same as reliably sampling from a specified distribution. A 2026 evaluation of 11 language models found serious weaknesses on that more demanding task—but its headline results do not give ChatGPT a standalone score.
Why are people asking whether ChatGPT always picks 17?
A community post described asking for random items and getting “17, 23, Saturn, triangle.” It is a memorable example, not a controlled test: one person’s account cannot establish that ChatGPT always chooses 17, or show how often any number appears across users and prompts. The post does capture a common frustration: an answer can look oddly patterned even when a user asked for randomness.
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The available evidence does not establish why a particular number recurs. It does not identify training data, prompt wording, decoding settings, or another factor as the cause. The more useful question is what kind of randomness the request actually requires.
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What does “random” mean when you ask a chatbot?
A plausible one-off choice
If you ask for one number for a casual decision, a chatbot can give you a choice. But seeing one answer that looks arbitrary does not demonstrate that the model selected it through a uniform random process—or any other distribution you had in mind.
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Sampling from a defined distribution
A stricter task specifies the possible outcomes and their probabilities: for example, choosing each integer in a range with equal probability. Testing that requires many samples and statistical checks. A model may produce a plausible-looking number or sequence without faithfully following those probabilities.
What did the 2026 language-model evaluation find?
An Association for Computational Linguistics paper by Minda Zhao, Yilun Du, and Mengyu Wang evaluated 11 language models across 15 probability distributions. Its abstract describes two protocols: generating a batch of 1,000 samples in one response, and making 1,000 stateless independent requests. The paper’s abstract and bibliographic record report the following headline findings:
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- Batch generation: The median pass rate across the tested models was 7%. This is a median for the study’s model set and batch protocol, not ChatGPT’s individual score.
- Independent requests: 10 of the 11 models passed none of the tested distributions under the study’s 1,000-stateless-call protocol. This is a result for that model set and distribution suite, not a claim about every ChatGPT version or every possible prompt.
The authors also report that sampling fidelity fell as distributions became more complex and as the sample horizon grew. Their abstract describes downstream problems in balancing multiple-choice answer positions and meeting demographic targets in constrained image-prompt synthesis. They conclude: “These findings indicate that current LLMs lack a functional internal sampler, necessitating external tools for applications requiring statistical guarantees.” That is the authors’ conclusion about the systems and protocols they studied, not an official verdict about every ChatGPT release.
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It supports caution about relying on ordinary language-model responses as statistically faithful samples. It does not, from the available abstract, establish a ChatGPT-only pass rate. The study’s summary identifies the number of models, distributions, protocols, and headline results, but does not provide an individual ChatGPT breakdown.
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To compare randomness claims fairly, check which model and version were tested; whether the task requested one number, a sequence, or samples from a defined distribution; whether samples came in one batch or in independent prompts; how many samples were collected; and what statistical test and pass criterion were used. Those distinctions can change what a result means.
Why does another study say ChatGPT-3.5 did well?
A preliminary 2024 study adapted a human random-number generation test and reported that ChatGPT-3.5 avoided repetitive and adjacent-number patterns more effectively than the human comparison group. That study examined a different task from testing whether a model faithfully samples specified probability distributions.
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There is no necessary contradiction. A system can produce sequences that look less repetitive than people’s choices in one test while still failing to match target probabilities across distributions in another. “Looks random” and “samples the requested distribution” are different standards.
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Was a ChatGPT bug responsible for repeated numbers?
OpenAI documented a specific incident in February 2024 in which incorrect inference-kernel results on certain GPU configurations affected number selection during language processing. The company says it deployed a fix and confirmed the incident was resolved. That incident write-up concerns a particular bug; it does not explain casual reports of repeated numbers generally.
What should you use when the outcome needs to be random?
For a low-stakes choice
If the consequences are trivial, asking a chatbot for a pick may be convenient—but treat it as a choice, not proof of a statistically fair draw. A physical die is a simple, visible alternative for a casual decision.
For statistical guarantees
Use an external tool designed for the specific requirement rather than relying on a conversational response. The right choice depends on what you need: uniform sampling, a particular probability distribution, reproducibility, or another guarantee. The cited evaluation supports using an appropriate external tool for applications that require statistical guarantees; it does not assess or endorse a particular generator.
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