OpenAI says its new o3 and o4-mini AI models hallucinate more often than its previous reasoning and traditional models, and the company doesn't know why
OpenAI's internal tests show o3 hallucinated on 33% of person-related questions, double the rate of previous models. Even worse, o4-mini hit 48%. Mastodon: Aulia Masna / @aulia@mementomori.social : “OpenAI found that o3 hallucinated in response to 33% of questions on PersonQA, the company's in-house benchmark for measuring the accuracy of a model's knowledge about people. That's roughly double the hallucination rate of OpenAI's previous reasoning models, o1 and o3-mini, which scored 16% and 14.8%, respectively. … X: Natsuki / @twtzero_ : alright party's over [image] @transluceai : We tested a pre-release version of o3 and found that it frequently fabricates actions it never took, and then elaborately justifies these actions when confronted. We were surprised, so we dug deeper 🔎🧵(1/) https://x.com/... [image] Ryan Lowe / @ryan_t_lowe : o3 seems to hallucinate >2x more than o1, according to the system card so hallucinations could scale *inversely* with increased reasoning (unlike for increased model size), bc outcome-based optimization incentivizes confident guessing (the Transluce example is kinda hilarious) [image] @modestproposal1 : man do some humans need this Daniel Litt / @littmath : First impressions of o3/o4-mini for math: tool use is really great; *lots* of hallucinations; underlying reasoning is maybe slightly better than o1/o3-mini or gemini 2.5 pro but I'm not confident about this. Peter Wildeford / @peterwildeford : Great thread. o3 makes meaningful progress for mathematical applications, excelling at undergrad problems and basic tool use... but still struggles with research-level mathematics, proof construction, and avoiding hallucinations. Nathan Lambert / @natolambert : reasoning models are kind of yolo and brining the fun back to AI caveat: lots of ways we dont know what happens when theyre in the world @transluceai : These behaviors are surprising. It seems that despite being incredibly powerful at solving math and coding tasks, o3 is not by default truthful about its capabilities. (12/) Aashay Sachdeva / @aashaysachdeva : o-series now mixes tool usage during training. This is definitely an issue stemming from complexity of training with multi-tool setup. Model is now hallucinating in the tools space. The AI safety research also got a lot more interesting - llms with tools have ability to [image] Dr. Angela Rasmussen / @angie_rasmussen : The model has improved and is now capable of making shit up about why it made shit up Neil Chowdhury / @chowdhuryneil : Another transcript: o3 confidently claims it executed code and defends its incorrect calculations. First, o3 tells me about its Python sandbox (which it does not have access to!) 🧵 (1/) [image] Alexander Doria / @dorialexander : Very insightful early tests of o3 showing that a top frontier “PhD-level” model remain totally unreliable for a wide variety of mundane tasks. Ethan Mollick / @emollick : A potential issue with o3 is that it thinks it is using tools even when it does not, leading to some hallucinations where it assumes work that was implied in the reasoning chain was actually done. You should double check the reasoning trace for complex work to see what it did. LinkedIn: Hari Stephen Kumar : OpenAI's more advanced reasoning models hallucinate MORE than older models. o4-mini hallucinates 48% of the time per one internal benchmark test. … Kian Katanforoosh : I had a great chat with Maxwell Zeff from TechCrunch about how we've been using o3 and o4-mini. I wanted to share three quick thoughts from that conversation: … Forums: Hacker News : OpenAI's new reasoning AI models hallucinate more r/technology : OpenAI Puzzled as New Models Show Rising Hallucination Rates r/BetterOffline : OpenAI's new reasoning AI models hallucinate more | TechCrunch r/ArtistHate : If this is not a sign that LLMs have peaked, I don't know what it is r/Futurism : OpenAI Puzzled as New Models Show Rising Hallucination Rates r/artificial : OpenAI's new reasoning AI models hallucinate more r/singularity : OpenAI's new reasoning AI models hallucinate more | TechCrunch r/OpenAI : OpenAI's new reasoning AI models hallucinate more Msmash / Slashdot : OpenAI Puzzled as New Models Show Rising Hallucination Rates
Context & Ripple Effects
OpenAI’s reasoning-model line was introduced as a distinct trade-off from GPT-4o, with earlier coverage noting cost and performance trade-offs in o1 rather than a simple across-the-board upgrade. The later o3 rollout extended that “think before responding” approach through a private chain-of-thought framing.
The new PersonQA results complicate the implied progression: higher reasoning-oriented benchmark performance does not necessarily translate into more reliable factual answers about people. Because OpenAI says it does not know the cause, the finding is a meaningful limitation rather than an explained design choice.
First-order effects
- Teams using o3 or o4-mini for person-related factual work face a higher measured need for verification; OpenAI’s own results put o3 at 33% and o4-mini at 48% on PersonQA, versus lower rates for o1 and o3-mini.
- OpenAI’s model-selection message becomes less linear: customers must weigh the newer models’ reasoning capabilities against factual-reliability performance instead of treating model recency as a default upgrade.
Second-order effects
- Enterprise buyers and application developers are likely to make evaluation and fallback routing more model- and task-specific, retaining earlier models where factual accuracy matters more than the newer models’ strengths.
- The result raises the value of operational safeguards—retrieval, source checks, human review, and post-deployment testing—because headline reasoning benchmarks alone do not establish dependable behavior in a particular workflow.
Third-order effects
- If divergent capability and hallucination profiles persist across frontier models, AI products will compete less on a single benchmark hierarchy and more on transparent, workload-specific assurance evidence.
- The broader market may increasingly treat model releases as configurable components requiring continuous evaluation, not as universally superior replacements; whether that becomes standard depends on whether providers can explain and reduce these regressions.
The trend: Frontier AI is shifting from benchmark-led model launches toward operational assurance, where reliability must be demonstrated separately for each task and model version.