Google AI systems like AI Overviews and Gemini demonstrate significantly higher factual accuracy than ChatGPT, particularly for local searches and brand-related queries. Research shows ChatGPT hallucination rates range from 12% to 48%, while Google’s search-grounded models achieve sub-3% error rates on factual consistency tasks.
In this article, we examine why these accuracy differences exist, what they mean for brands tracking AI search visibility, and how to optimize content for more reliable AI responses.
Why does Google AI produce fewer hallucinations than ChatGPT?
The difference is in architecture. Google AI systems use what researchers call a “grounded search” approach, actively retrieving real-time data and cross-referencing against verified databases. ChatGPT primarily relies on pre-trained knowledge with optional web search.
How grounded search reduces hallucinations
Google AI Mode and AI Overviews connect directly to Google’s search infrastructure, including:
- Real-time web indexing
- Google Maps local business data
- Knowledge Graph entity verification
- Structured data from verified sources
This grounding means Google AI can verify claims against current information rather than generating plausible-sounding responses from pattern matching alone.
According to the Stanford Human-Centered AI Institute’s 2025 report, models using retrieval-augmented generation (RAG) techniques reduce hallucinations by 40% to 71% compared to purely generative approaches.
ChatGPT’s dual-mode problem
ChatGPT operates in two modes: internal knowledge generation and live web search. When users don’t explicitly trigger web search, ChatGPT relies entirely on training data. This creates a critical vulnerability.
The December 2025 Relum reliability study measured hallucination rates across major LLMs:
| LLM Model | Hallucination Rate | Reliability Risk Score |
|---|---|---|
| Grok | 8% | 6 |
| DeepSeek | 14% | 4 |
| ChatGPT | 35% | 99 |
| Gemini | 38% | Variable |
The study assigned ChatGPT the maximum reliability risk score of 99, indicating significant potential for factual errors in enterprise applications.
How do hallucination rates differ by query type?
Not all queries trigger equal error rates. Research from AllAboutAI’s 2025 hallucination report reveals that domain-specific queries produce dramatically different results:
| Query Domain | Top Model Rate | All Models Average |
|---|---|---|
| General Knowledge | 0.8% | 9.2% |
| Financial Data | 2.1% | 13.8% |
| Medical/Healthcare | 4.3% | 15.6% |
| Scientific Research | 3.7% | 16.9% |
| Legal Information | 6.4% | 18.7% |
Local business queries: ChatGPT’s weakness
ChatGPT demonstrates particular vulnerability when generating local business recommendations. Without access to real-time local databases, it often fabricates business names, addresses, or operating hours that sound plausible but don’t exist.
Google’s AI systems avoid this through direct integration with Google Maps and local business listings. When a user asks “best restaurants near me,” Google AI pulls from verified business profiles rather than generating probabilistic guesses.
What do Superlines data show about accuracy across platforms?
Our tracking of 192,423 AI responses reveals how accuracy differences affect citation and visibility patterns:
| LLM Platform | Total Responses | Citation Rate | Brand Visibility |
|---|---|---|---|
| Google AI Mode | 29,838 | 13.47% | 11.67% |
| Google AI Overview | 28,958 | 1.72% | 7.70% |
| ChatGPT | 29,011 | 1.37% | 8.18% |
| Gemini | 15,793 | 4.82% | 9.61% |
Source: Superlines analysis, December 2025 to January 2026.
Google AI Mode cites external sources at nearly 10x the rate of ChatGPT. This higher citation rate correlates with Google’s grounded approach: when responses are anchored to verifiable sources, those sources get cited.
ChatGPT’s low citation rate (1.37%) reflects its reliance on training data rather than live retrieval. Responses generated from internal knowledge don’t require citations because they aren’t drawn from specific sources.
Why does this matter for AI search visibility?
The accuracy gap creates strategic implications for brands optimizing for AI search:
High-accuracy platforms reward structured content
Google AI systems preferentially cite sources with structured data, clear entity information, and verifiable facts. According to Google’s AI documentation, AI Overviews prioritize sources that demonstrate topical authority through comprehensive, well-organized content.
For brands, this means:
- Schema markup becomes more valuable for AI visibility
- Factual accuracy in content directly affects citation likelihood
- Local business data in Google Business Profile feeds AI responses
Low-accuracy platforms matter for brand mentions
ChatGPT’s 8.18% brand visibility rate suggests it mentions brands frequently despite its low citation rate. This creates a different optimization target: training data presence rather than citable content.
Claude shows similar patterns with 9.38% brand visibility against just 0.31% citation rate. These platforms recommend brands conversationally without providing source links.
How are hallucination rates changing over time?
The Stanford HAI 2025 AI Index Report documents significant improvement in top-tier models:
- 2021 average hallucination rate: 38%
- 2026 leading model rates: 0.7% to 1.9%
Google’s Gemini-2.0-Flash-001 achieved the benchmark’s lowest hallucination rate at 0.7% as of April 2025. Four models now operate below 1% error rates on standardized factual consistency tests.
However, improvement isn’t universal. OpenAI’s newer reasoning-focused models (o3, o4-mini) showed increased hallucination rates of 33% to 48% on certain benchmarks, suggesting that advanced reasoning capabilities may trade off against factual precision.
What causes the grounded vs generative accuracy gap?
Understanding the technical differences helps explain why some AI systems hallucinate more than others:
Retrieval-augmented generation (RAG)
Google AI systems implement RAG architecture, which works in three steps:
- Query interpretation and search
- Document retrieval from verified sources
- Response generation grounded in retrieved content
This architecture constrains outputs to information that exists in verifiable sources, dramatically reducing fabrication.
Pure autoregressive generation
ChatGPT and Claude primarily use autoregressive generation, predicting each word based on probability distributions learned during training. When training data lacks information about a topic, these models generate statistically likely completions rather than acknowledging uncertainty.
The Master of Code research on LLM hallucinations notes that “LLMs are trained to predict the next most likely token based on patterns in training data, not to verify truth.”
How should brands optimize for accurate AI platforms?
Different accuracy profiles require different optimization strategies:
For Google AI (grounded systems)
Focus on citable, verifiable content:
- Implement comprehensive schema markup (Article, FAQ, Organization)
- Ensure Google Business Profile accuracy for local queries
- Include specific statistics with source citations
- Structure content with clear hierarchical headings
- Update content regularly to maintain freshness signals
For ChatGPT and Claude (generative systems)
Focus on training data presence:
- Build brand authority through consistent messaging across multiple sources
- Ensure accurate brand information exists in commonly crawled sources
- Monitor brand mentions for accuracy rather than citations
- Track sentiment to understand how AI systems discuss your brand
What does this mean for measuring AI search performance?
The accuracy gap affects which metrics matter for each platform type:
| Platform Type | Primary Metric | Secondary Metric |
|---|---|---|
| Google AI (grounded) | Citation rate | Source diversity |
| ChatGPT/Claude (generative) | Brand visibility | Mention sentiment |
| Grok/Perplexity (hybrid) | Both citations and visibility | Response consistency |
Tracking tools like Superlines monitor both citation rates and brand visibility across platforms, enabling comparison of performance where each metric matters most.
Key takeaways
- Google AI achieves higher accuracy through grounded search architecture that verifies responses against real-time data
- ChatGPT hallucination rates range from 12% to 35% depending on query type, with local and specialized queries showing highest error rates
- Citation rates correlate with accuracy as Google AI Mode cites sources at 10x the rate of ChatGPT
- Different platforms require different strategies: optimize for citations on grounded systems, brand mentions on generative systems
- Top models now achieve sub-1% error rates on factual tasks, but advanced reasoning models may trade accuracy for capability
Methodology
This analysis combines external research from Stanford HAI, Relum, AllAboutAI, and Master of Code with internal Superlines tracking data covering 192,423 AI responses from December 2025 to January 2026 across 50 tracked brands.
Frequently asked questions
Why does ChatGPT hallucinate more than Google AI?
ChatGPT relies primarily on pre-trained knowledge and predicts statistically likely responses. Google AI actively retrieves and verifies information against current databases, reducing fabrication. The architectural difference explains the accuracy gap.
Which AI assistant is most accurate for local searches?
Google AI systems (AI Mode and AI Overviews) demonstrate highest accuracy for local queries because they connect directly to Google Maps and verified business listings. ChatGPT frequently fabricates local business details when lacking specific data.
Do hallucination rates affect AI search visibility?
Yes. Platforms with higher accuracy tend to cite more sources, meaning brands with citable content see better visibility on grounded systems like Google AI. On generative systems like ChatGPT, brand mention frequency matters more than citation rate.
How can brands reduce the risk of AI hallucinations about them?
Ensure accurate brand information exists across authoritative sources: Wikipedia, industry publications, your own website with proper schema markup, and Google Business Profile. The more verified sources containing correct information, the less likely AI systems will fabricate details.
Is AI hallucination getting better or worse?
Top-tier models improved from 38% average hallucination rate in 2021 to sub-1% rates in 2026 on standardized tests. However, newer reasoning-focused models show higher error rates on some benchmarks, suggesting improvement isn’t uniform across all model types.