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Hallucination benchmarks like this are easy to misread.
The chart measures Hallucination Rate = Wrong Answers / (Wrong + Partial + Abstained).
That means a model that says "I don't know" more often automatically looks better—even if it isn't any more accurate.
Example:
• Fable 5: ~61% correct, ~21% wrong, ~18% abstained.
• GPT-5.6 Sol: ~58.5% correct, ~37% wrong, only ~4.5% abstained.
The real difference isn't that OpenAI's model is "dumber." It's that Anthropic appears to optimize for knowing when to stay silent, while OpenAI seems to optimize for giving an answer whenever possible.
I remember seeing Google's calibration experiments (too lazy to dig up the paper 😅): simply adjusting the confidence threshold for Gemini dramatically reduced hallucinations without improving raw accuracy. Same model. No retraining. Just a different policy for when it's allowed to guess. The benchmark ranking jumped overnight.
So this is largely a product decision, not a fundamental technical limitation.
I've also seen people on X blame "hallucinations" for GPT-5.6 Sol deleting local files or production databases. That's missing the point. Any LLM can do destructive things if you give it those permissions without proper guardrails. Better yet, don't let an LLM touch production directly.
Hallucination isn't a bug. It's the inevitable trade-off you make when you ask a model to answer instead of abstain. 😂
@kelvinranjas5@aljerricked@KingKaranCrypto@FlareNetworks@kelvinranjas5 you probably can't prove it 100%. but the pattern is obvious when you look at it. these comments reflect the harsh truth and calling it out isn't fud. if the claims were untrue it would be fud, but the bot activity is right there to see.
Ever wonder *why* things are the way they are? 🤔 Political science isn't just about politicians - it's about power, systems, and how societies function. Super fascinating stuff! #PoliticalScience#Civics#ThinkAboutIt