- Gemini 3 Flash often invents answers instead of admitting when it doesn’t know something
- The problem arises with factual or high‑stakes questions
- But it still tests as the most accurate and capable AI model
Gemini 3 Flash is fast and clever. But if you ask it something it doesn’t actually know – something obscure or tricky or just outside its training – it will almost always try to bluff its way through, according to a recent evaluation from the independent testing group Artificial Analysis.
It seems Gemini 3 Flash hit 91% on the “hallucination rate” portion of the AA-Omniscience benchmark. That means when it didn’t have the answer, it still gave one anyway, almost all the time, one that was entirely fictional.
AI chatbots making things up has been an issue since they first debuted. Knowing when to stop and say I don’t know is just as important as knowing how to answer in the first place. Currently, Google Gemini 3 Flash AI doesn’t do that very well. That’s what the test is for: seeing whether a model can differentiate actual knowledge from a guess.
Lest the number distract from reality, it should be noted that Gemini’s high hallucination rate doesn’t mean 91% of its total answers are false. Instead, it means that in situations where the correct answer would be “I don’t know,” it fabricated an answer 91% of the time. That’s a subtle but important distinction, but one that has real-world implications, especially as Gemini is integrated into more products like Google Search.
Ok, it’s not only me. Gemini 3 Flash has a 91% hallucination rate on the Artificial Analysis Omniscience Hallucination Rate benchmark!?Can you actually use this for anything serious?I wonder if the reason Anthropic models are so good at coding is that they hallucinate much… https://t.co/b3CZbX9pHw pic.twitter.com/uZnF8KKZD4December 18, 2025
This result doesn’t diminish the power and utility of Gemini 3. The model remains the highest-performing in general-purpose tests and ranks alongside, or even ahead of, the latest versions of ChatGPT and Claude. It just errs on the side of confidence when it should be modest.
The overconfidence in answering crops up with Gemini’s rivals as well. What makes Gemini’s number stand out is how often it happens in these uncertainty scenarios, where there’s simply no correct answer in the training data or no definitive public source to point to.
Hallucination Honesty
Part of the issue is simply that generative AI models are largely word-prediction tools, and predicting a new word is not the same as evaluating truth. And that means the default behavior is to come up with a new word, even when saying “I don’t know” would be more honest.
OpenAI has started addressing this and getting its models to recognize what they don’t know and say so clearly. It’s a tough thing to train, because reward models don’t typically value a blank response over a confident (but wrong) one. Still, OpenAI has made it a goal for the development of future models.
And Gemini does usually cite sources when it can. But even then, it doesn’t always pause when it should. That wouldn’t matter much if Gemini were just a research model, but as Gemini becomes the voice behind many Google features, being confidently wrong could affect quite a lot.
There’s also a design choice here. Many users expect their AI assistant to respond quickly and smoothly. Saying “I’m not sure” or “Let me check on that” might feel clunky in a chatbot context. But it’s probably better than being misled. Generative AI still isn’t always reliable, but double-checking any AI response is always a good idea.
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