Benchmark Almanac

BirthdayFacts

o1-mini 24% Only in system cards

Models are asked for people's birthdays. Scored on how often they give a wrong birthday.

In-house OpenAI hallucination evaluation.

Made by OpenAI Released Sep 2024 Hallucination rates Tasks kept private Last in a system card Sep 2024 Last model added Sep 2024
Level
Everyday Most adults, without training
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Short answer A number, word or short string matched to a key.
2
Models scored in lab cards
2
System cards report it, from 1 lab

o1-mini leads with 24%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 o1-mini 24%
  2. 2 o1-preview 32%
#ModelScoreSettingDateSource
1 o1-mini 24% Sep 2024 Model card
2 o1-preview 32% Sep 2024 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% o1-mini o1-preview 0% 20% 40% 60% 80% 100% o1-mini o1-preview

Labs report it only in system cards

We read the headline results of 479 launch posts from 51 labs, and none of them list this benchmark. Some system cards do, below.

2 system cards report it, from 1 lab

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
12 Sep 2024 o1-preview system card 32 BirthdayFacts, hallucination rate, lower is better (Table 2) Hallucination
12 Sep 2024 o1-mini system card 24 BirthdayFacts, hallucination rate, lower is better (Table 2) Hallucination

More in Factuality

All 70 in Factuality
  • SimpleVQA In use Models answer 2,025 short factual questions about images across 9 tasks and 9 topics. An LLM judge grades answers against fixed references. Qwen3.7-Plus 81.7%
  • HallusionBench In use Models answer 1,129 yes or no questions about 346 images, built in pairs to expose language hallucination and visual illusion. Scored by accuracy. Keye-VL-2.0-30B-A3B 73.5%
  • SimpleQA Verified In use 1,000 human-verified, de-duplicated short-answer factual questions measuring parametric knowledge. Gemini 3.1 Pro 77.5%
  • GaRAGe In use Models write long-form RAG answers to 2,366 questions with human-annotated grounding passages. Scoring checks whether answers use the relevant passages. Nemotron 3.5 Bolt E4B 40.7%
  • Kimi Hallu Bench In use Models answer hard prompts drawn from practical work tasks. Fact-checking agents score the share of claims that are hallucinated. Kimi K3.1 Pro 8.30%
  • RAGTruth In use A word-level annotated corpus of hallucinations in retrieval-augmented generation answers. It is used to train and evaluate hallucination detection and RAG faithfulness. Nemotron 3.5 Bolt E4B 77.9%