"Which AI model is most accurate?" is the most common question people ask when comparing LLMs. And the answer is both simpler and more complicated than most comparison articles admit.
Simple version: Claude 4 Sonnet has the lowest hallucination rate and strongest domain-specific accuracy in our tests. Gemini 2.5 Flash leads on recent factual knowledge. GPT-5 Mini is the most consistent at instruction-following tasks.
Complicated version: accuracy is deeply task-dependent, and every model makes different mistakes. No single model is most accurate across all question types, which means relying on one model is always a risk.
Key finding: In our 200-question test, no model achieved above 94% accuracy on any category. And the questions each model got wrong were different questions, meaning a consensus of all five models would have outperformed any individual model alone.
How We Measured Accuracy
We tested five models, GPT-5 Mini, Claude 4 Sonnet, Gemini 2.5 Flash, Sonar Pro, and Grok 3 Mini, across 200 questions divided into four categories: general knowledge, domain-specific (medical, legal, scientific), mathematical reasoning, and recent events (2025–2026). Each answer was verified against authoritative primary sources.
Accuracy by Category
General Knowledge Accuracy
Domain-Specific Accuracy (Medical, Legal, Scientific)
Mathematical Reasoning
Hallucination Rates
Hallucination, producing confident, plausible-sounding but factually wrong information, is the most dangerous accuracy problem in LLMs. Here are our approximate hallucination rates across the full 200-question test set:
- Claude 4 Sonnet: ~8% hallucination rate (lowest in our test)
- Gemini 2.5 Flash: ~10%
- GPT-5 Mini: ~12%
- Sonar Pro: ~9% (web-search grounded answers significantly reduce this)
- Grok 3 Mini: ~14%
Important caveat: These hallucination rates vary significantly by domain. All models hallucinate more on niche topics, recent events beyond their training cutoff, and highly specific technical details. No model should be trusted as a sole source for high-stakes decisions.
The Most Important Accuracy Finding
Here's what surprised us most: when we looked at which specific questions each model got wrong, the overlap was small. Claude's wrong answers were mostly different from GPT's wrong answers, which were different from Gemini's wrong answers.
This means that if you run all five models on the same question and see 4–5 agreeing on the same answer, the probability that answer is wrong is dramatically lower than any single model's error rate would suggest. This is the statistical foundation of consensus-based AI verification.
In our test set, when 5 out of 5 models agreed on an answer, the accuracy was above 97%. When 4 out of 5 agreed, it was above 93%. A single model's average accuracy: 87–92%.
So, Which AI Is Most Accurate?
For domain-specific expertise (medical, legal, scientific): Claude 4 Sonnet. For math and current events: Gemini 2.5 Flash. For consistent, instruction-faithful output: GPT-5 Mini.
But the most accurate answer you can get comes from running all of them together and trusting the consensus, not from picking a winner and hoping it doesn't make a mistake that day.
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talkory.ai queries all five models at once and gives you a confidence score based on their agreement. When they agree, you can trust the answer. When they disagree, you know to verify.
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