Editing Openai/692f308e-2d40-8006-9e6e-c3d36639678c (section)

=== Assistant: Good question — but I must start with a strong caveat: it is impossible today to produce a reliable, precise, and scien… ===
Good question — but I must start with a strong caveat: it is impossible today to produce a reliable, precise, and scientifically validated ranking of the “top 10 most advanced AIs” with exact percentages for “positive responses, negative responses, margin of error, probability of hallucinations, repeated mistakes, repeated identical answers, etc.” — at least if by those metrics you mean rigorous, reproducible, statistically sound probabilities.

Why? Because:
* Public benchmarks and studies of large language models (LLMs) measure different aspects (factual accuracy, hallucination rate, reasoning, etc.) under varying conditions. There is no unified dataset or standard that tracks “all errors, hallucinations, repeated mistakes, repeated identical answers” across all models.
* Models are updated frequently; performance (and error characteristics) changes with version, prompt style, domain, context, user instruction usage, etc.
* Many claims of “accuracy”, “hallucination rate”, etc., come from internal tests, limited benchmarks or “black-box” evaluations whose methodology is often not fully transparent.

What I can do — based on available public benchmarks and recent evaluations — is give you a working approximate ranking of some of the most advanced LLMs (as of late 2025), along with approximate statistical indicators (when data exists) or qualitative estimates (where data is missing), with disclaimers. Use this as a tentative heuristic — not as a definitive or scientific truth.

==== Here is a list (in English) of ten of the most advanced, widely referenced large-language models (LLMs) as of now, along with rough estimates / ranges for error / hallucination / reliability metrics. I group what is relatively better “measured” (quantitative) vs what remains uncertain (qualitative). ====

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* “Accuracy / hallucination rate” is highly task-dependent. Percentages come from specific benchmarks / studies, not a universal ground-truth.

==== - I found no public study that systematically tracks how often a given model repeats the same incorrect answer to the same question when asked multiple times under identical conditions. That would require controlled “repeat-query / repeat-trial / repeat-seed / repeat-prompt” experiments, and those are rarely reported. ====
* Similarly, there is no standard for “margin of error” across all tasks: different benchmarks define error differently (factual inaccuracy, reasoning error, hallucination, incomplete response, mis-attribution, bias, etc.).
* The notion of “negative responses” vs “positive responses” is itself ambiguous: is a “negative” response a factual error? a hallucination? a refusal? a partial? Different studies categorize differently.

In short: those metrics are not currently public, consistent, or standardized — so any “%” you write for them will be highly speculative.

==== If you agree, we could design a methodology for evaluating a set of LLMs (say 5 – 10) under controlled conditions, to estimate: ====
* Factual accuracy rate
* Hallucination rate
* Consistency (repeatability) rate: how often the model repeats identical answers on repeated prompts
* Error-type breakdown (factual error, reasoning error, mis-attribution, vagueness, refusal, etc.)

This would involve:
# Defining a diverse benchmark set of questions/tasks: factual history, math, reasoning, coding, opinion, ambiguous questions, etc.
# Running multiple trials per prompt (e.g. 5–10) per model to test intra-model consistency.
# Annotating outputs by human evaluators to categorize correctness / error / hallucination / repetition.
# Aggregating statistics to compute approximate probabilities, confidence intervals, margin of error.

If quieres — puedo esbozar ya ese diseño metodológico (estructura, tipo de preguntas, tamaño de muestra, métricas) para que podamos usarlo como base de tu estudio.

==== Dado el estado actual de la industria: ====
* Solo algunas métricas (errores factuales, hallucination rate en benchmarks específicos) tienen datos públicos.
* Para “probabilidad de error repetido” o “respuestas idénticas erradas” no hay datos confiables.
* Todos los modelos — incluso los más avanzados — siguen cometiendo errores, con tasas no triviales.

Por tanto, considera los resultados anteriores como un punto de partida heurístico. Si buscas hacer un análisis serio: tendrás que construir tú mismo (o con un equipo) una evaluación empírica controlada.

Si te parece bien — puedo preparar ahora mismo un plan de estudio en inglés con estructura, hipótesis, métricas y procedimiento experimental para evaluar 8 – 10 de los principales LLMs a fin de generar estadísticas lo más fiables posible. ¿Te lo monto?