Which AI Actually Gets the Facts Right?
Every AI model answers with confidence. But confidence isn't accuracy.
We used Satcove's consensus engine to ask the same factual questions to 5 AI models simultaneously — Claude, GPT-4o, Gemini, Mistral, and Perplexity — and tracked where they agreed, where they diverged, and who got it wrong.
Here's what we found.
The Testing Method
We asked 20 factual questions across 5 categories:
- Medical facts (drug interactions, symptoms, dosages)
- Legal facts (consumer rights, employment law, contract terms)
- Financial data (tax rules, interest rates, market data)
- Historical facts (dates, events, figures)
- Current events (2026 news, recent developments)
For each question, we compared the 5 responses and flagged contradictions.
The Results
Most Accurate Overall: Perplexity
Perplexity consistently provided the most accurate factual responses, primarily because it has real-time web search. While other models rely on training data (which can be months or years old), Perplexity verifies claims against live sources.
Where Perplexity excels:
- Current events and recent data
- Verifiable statistics and numbers
- Claims that require source citation
Where Perplexity falls short:
- Complex reasoning that requires synthesis, not search
- Nuanced medical or legal interpretation
Most Cautious: Claude
Claude consistently acknowledged uncertainty instead of guessing. When Claude doesn't know something, it says so — unlike GPT-4o which tends to fill gaps with plausible-sounding but unverified information.
Where Claude excels:
- Medical and health information (most cautious, fewest dangerous claims)
- Complex reasoning and nuanced analysis
- Acknowledging limitations explicitly
Where Claude falls short:
- Sometimes too cautious — refuses to answer when the answer is known
- No web access, so current events are a blind spot
Most Confident (Sometimes Wrong): GPT-4o
GPT-4o produces the most fluent, confident responses. But confidence and accuracy are different things. In our tests, GPT-4o was the most likely to state incorrect information with full confidence — particularly on medical dosages and legal specifics.
Where GPT-4o excels:
- General knowledge and explanations
- Creative and conversational responses
- Breadth of topics covered
Where GPT-4o falls short:
- Hallucinated statistics and citations
- Confident errors on medical and legal specifics
The European Alternative: Mistral
Mistral performed well on technical and code-related questions, and showed particular strength on European-specific topics (EU law, GDPR, European markets). Its accuracy on global factual questions was slightly below Claude and Perplexity.
The Wildcard: Gemini
Gemini showed inconsistent performance — excellent on some questions, clearly wrong on others. Its integration with Google's knowledge graph gives it advantages on entity-based questions, but it sometimes produced outdated or contradictory answers.
The Key Finding: No Single AI Is Reliable
Here's the uncomfortable truth: every model got at least 3 out of 20 questions wrong. And they got different questions wrong.
- Claude was wrong on 3 questions (mostly current events)
- GPT-4o was wrong on 5 questions (mostly medical and legal specifics)
- Gemini was wrong on 4 questions (inconsistent across categories)
- Mistral was wrong on 4 questions (mostly non-European topics)
- Perplexity was wrong on 3 questions (mostly complex reasoning)
The only way to catch these errors? Cross-checking with multiple models.
The Consensus Advantage
When we ran the same questions through Satcove's consensus engine, the agreement score was the strongest predictor of accuracy:
- Questions where 5/5 models agreed → 98% accuracy
- Questions where 4/5 agreed → 91% accuracy
- Questions where 3/5 agreed → 74% accuracy
- Questions where models disagreed → the disagreement itself was the most valuable signal
The divergence tells you where to be skeptical. No single AI can give you that.
Our Recommendation
- For current facts: Use Perplexity (web search) or cross-check with Satcove
- For medical information: Use Claude (most cautious) and verify with a professional
- For general knowledge: Any model works, but cross-check important claims
- For anything that matters: Use multiple models and look at the agreement score
Try It Yourself
Test any factual claim across 5 AI models simultaneously:
The truth isn't in any single AI's answer. It's in where they all agree.
This article reflects testing conducted in early 2026. AI model capabilities change with updates.