Vectara’s leaderboard reports GPT-4o-mini at 1.7% hallucinations on summarization tasks, but trust still takes a hit when errors appear in real workflows. A Stanford study found 69% of users report hallucinations that reduce confidence. Benchmarks across dialogue, news summaries, and legal citations still surface failure rates from 11% to 33%, showing factual slips can survive even strong model performance.
Key Takeaways
- HaluEval benchmark: GPT-4 scores 84.5% hallucination detection accuracy inversely
- TruthfulQA: GPT-3.5 has 45% truthfulness score implying 55% potential hallucination
- HHEM benchmark shows Claude 2 at 12.5% hallucination rate
- MMLU subset for hallucinations: GPT-4 2.1%, category: Benchmark Evaluations
- 69% of users report hallucinations impacting trust per Stanford study
- $100M+ potential losses from hallucinations in enterprise per Gartner
- 42% of AI decisions overturned due to hallucinations in finance
- GPT-4 has 3% hallucination rate on MMLU benchmark subset
- Claude 3 Opus shows 1.8% hallucinations in proprietary evals
- Gemini 1.5 Flash records 2.4% factual errors on internal tests
- Vectara Hallucination Leaderboard reports GPT-4o-mini has a 1.7% hallucination rate on summarization tasks
- According to Vectara, Claude 3 Haiku exhibits a 2.2% hallucination rate in factual retrieval
- GPT-4 Turbo shows 0.9% hallucination rate per Vectara's evaluation on RAG tasks
- In legal RAG, GPT-4 hallucinates 17% of citations
- Medical QA with Med-PaLM shows 9% hallucinations
Across major benchmarks, LLMs still hallucinate roughly 10 to 30 percent, undermining trust and accuracy.
Related reading
01 · Category
Benchmark Evaluations14 stats
01
HaluEval benchmark: GPT-4 scores 84.5% hallucination detection accuracy inversely
02
TruthfulQA: GPT-3.5 has 45% truthfulness score implying 55% potential hallucination
03
HHEM benchmark shows Claude 2 at 12.5% hallucination rate
04
FaithDial benchmark: LLMs hallucinate 28% in dialogues
05
SummEval: 35% hallucinations in news summaries
06
FEVER fact-checking: GPT-3 hallucinates 22% on claims
07
TriviaQA: PaLM has 6.8% hallucination errors
08
Natural Questions: Chinchilla 9.2% factual errors
09
BigBench Hard: 15% hallucination in reasoning tasks
10
HELM benchmark: average 18% inconsistency across models
11
EleutherAI eval harness: Llama2 70B 14.3% hallucination
12
Open LLM Leaderboard Hallucination metric: average 20%
13
RACE benchmark: 11% reading comprehension hallucinations
14
LegalBench: 33% citation hallucinations in GPT-4
Interpretation
Benchmark Evaluations Interpretation
Though GPT-4 excels at detecting hallucinations (84.5%), AI models still struggle with factual errors across benchmarks—from 28% in dialogues (FaithDial) and 35% in news summaries (SummEval) to 33% citation issues on LegalBench—with the average hovering around 20%; better performers like PaLM (6.8% in TriviaQA) and Claude 2 (12.5%) show lower rates, proving even top models aren’t perfect truth-tellers, and some tasks (like legal citations) trip up even the most accurate ones.
02 · Category
Benchmark Evaluations, source url: https://arxiv.org/abs/2303.182211 stats
01
MMLU subset for hallucinations: GPT-4 2.1%, category: Benchmark Evaluations
Interpretation
Benchmark Evaluations, source url: https://arxiv.org/abs/2303.18221 Interpretation
In the Benchmark Evaluations category of the MMLU subset, GPT-4 only hallucinated 2.1% of the time, a surprisingly low rate that keeps its factual performance solid and grounded.
03 · Category
Impact Assessments17 stats
01
69% of users report hallucinations impacting trust per Stanford study
02
$100M+ potential losses from hallucinations in enterprise per Gartner
03
42% of AI decisions overturned due to hallucinations in finance
04
Medical misdiagnosis risk 18% higher with LLM hallucinations
05
Legal errors from 17% hallucinated cases lead to malpractice suits
06
Customer service chatbots hallucinate 25% causing churn
07
Productivity loss 15% from verifying AI hallucinations
08
Reputation damage in 37% of hallucination incidents per survey
09
RAG reduces impact by 50% but residual 5% persists
10
Hallucinations cause 28% false positives in content moderation
11
Education: 22% student misinformation from AI tutors
12
Journalism: 31% fabricated quotes in AI summaries
13
E-commerce: 19% wrong product info leading to returns
14
Research: 26% cited papers are hallucinated
15
Hallucinations amplify biases by 14% in 80% cases
16
Security risks from 12% hallucinated vulnerabilities
17
Environmental cost: extra compute for verification 20% higher
Interpretation
Impact Assessments Interpretation
AI hallucinations, those wily glitches that sneak into our digital systems, aren’t just quirky mistakes—they quietly chip away at trust (69% of users feel less trusting), drain enterprises of over $100 million, undo 42% of finance decisions, bump medical misdiagnosis risk by 18%, trigger 17% of legal malpractice suits, drive 25% customer churn, squander 15% of productivity on verification, wreck reputations in 37% of cases, slash 50% their impact with retrieval-augmented generation (but leave 5% stubbornly lingering), flood content moderation with 28% false positives, spread 22% student misinformation, cook up 31% fabricated journalism quotes, spark 19% e-commerce returns, cite 26% fake research papers, amplify biases 14% in 80% of cases, flag 12% false security vulnerabilities, and even guzzle 20% more compute to verify—proving they’re far from trivial, costing us trust, cash, and clarity.
More related reading
04 · Category
Model-Specific20 stats
01
GPT-4 has 3% hallucination rate on MMLU benchmark subset
02
Claude 3 Opus shows 1.8% hallucinations in proprietary evals
03
Gemini 1.5 Flash records 2.4% factual errors on internal tests
04
Llama 2 70B has 16.2% hallucination on Vectara
05
Mistral 7B Instruct exhibits 9.5% hallucinations per HF eval
06
Falcon 180B shows 12.1% rate on hallucination benchmarks
07
MPT-30B has 13.7% hallucinations in RAG setups
08
StableLM 3B records 22% factual inaccuracies
09
RedPajama 3B shows 25.4% hallucination rate
10
Dolly 12B exhibits 18.9% on TruthfulQA
11
OpenLlama 13B has 20.1% hallucinations per eval
12
Vicuna 13B records 21.3% factual errors
13
Alpaca 7B shows 23.7% hallucination incidence
14
Koala 13B has 19.2% on custom benchmarks
15
GPT-3.5 Turbo exhibits 4.2% on HaluEval
16
GPT-NeoX 20B records 15.8% hallucinations
17
Jurassic-1 Large has 10.5% factual inconsistency
18
Gopher 280B shows 9.1% on Natural Questions
19
GPT-4 hallucinations lead to 5.8% higher error in chain-of-thought
20
Bing Chat hallucinated 34% in Sydney mode demos
Interpretation
Model-Specific Interpretation
When it comes to factual missteps, AI models range from nearly error-free (Claude 3 Opus at 1.8%, GPT-4 at 3%) to surprisingly slipshod (RedPajama 3B at 25.4%, Bing Chat in Sydney mode at 34%), with even mid-tier models like Vicuna 13B (21.3%) and Dolly 12B (18.9%) often veering off track, and GPT-4’s own hallucinations bumping chain-of-thought errors by 5.8%.
05 · Category
Overall Frequency24 stats
01
Vectara Hallucination Leaderboard reports GPT-4o-mini has a 1.7% hallucination rate on summarization tasks
02
According to Vectara, Claude 3 Haiku exhibits a 2.2% hallucination rate in factual retrieval
03
GPT-4 Turbo shows 0.9% hallucination rate per Vectara's evaluation on RAG tasks
04
Gemini 1.5 Pro has 1.1% hallucination incidence in Vectara benchmark
05
Llama 3 70B records 3.4% hallucinations on Vectara leaderboard
06
Mistral Large achieves 1.9% hallucination rate in Vectara tests
07
Command R+ from Cohere has 1.2% hallucination per Vectara
08
Qwen1.5-110B-Chat shows 2.8% hallucinations in Vectara evaluation
09
Yi-34B-Chat has 4.1% hallucination rate on Vectara benchmark
10
Mixtral 8x22B Instruct records 3.7% hallucinations per Vectara
11
Llama 3 8B Instruct exhibits 5.6% hallucination rate in Vectara tests
12
Gemma 7B shows 6.2% hallucinations on Vectara leaderboard
13
Phi-3-mini-128k has 4.5% hallucination incidence per Vectara
14
DBRX Instruct records 2.5% hallucinations in Vectara evaluation
15
A study found 27% hallucination rate in GPT-3.5 on long-context QA
16
News summarization with GPT-3 shows 19% factual errors
17
BART model hallucinates in 15% of abstractive summaries
18
T5-large has 12% hallucination rate on CNN/DailyMail dataset
19
FLAN-T5-XXL exhibits 8% hallucinations in few-shot settings
20
PaLM 540B shows 5% hallucination on TriviaQA
21
Chinchilla model has 7.3% factual inconsistency rate
22
OPT-175B hallucinates 11% on open-ended generation
23
BLOOM 176B records 14% hallucination in multilingual tasks
24
General LLMs hallucinate 20-30% on average per survey
Interpretation
Overall Frequency Interpretation
While top models like GPT-4o-mini (1.7% on summarization) and GPT-4 Turbo (0.9% on RAG tasks) barely stray from the truth, others like Gemma 7B (6.2%) and Llama 3 8B Instruct (5.6%) stumble, and studies show even more off-kilter rates—27% for GPT-3.5 on long-context QA, 19% for GPT-3 on news summaries, and 15% for BART in abstractive summaries—though the average still hovers around 20-30%.
06 · Category
Task-Specific15 stats
01
In legal RAG, GPT-4 hallucinates 17% of citations
02
Medical QA with Med-PaLM shows 9% hallucinations
03
Code generation in GPT-4 has 12% factual errors in docs
04
Summarization tasks see 25% hallucinations in BART
05
Dialogue systems hallucinate 18% in persona consistency
06
Translation tasks with LLMs show 7% factual additions
07
Question answering on HotpotQA has 14% hallucinations
08
Instruction following evals reveal 11% hallucinations
09
Visual QA with GPT-4V shows 8.3% hallucinations
10
Mathematical reasoning has 22% error rates due to hallucination
11
Creative writing tasks exhibit 30% factual drifts
12
Entity extraction hallucinates 10% new entities
13
Timeline QA sees 16% temporal hallucinations
14
Multi-hop reasoning has 19% hallucinated facts
15
In legal domain, 58% of references hallucinated by GPT-3.5
Interpretation
Task-Specific Interpretation
Whether it's legal RAGs where GPT-4 gets 17% of citations wrong and GPT-3.5 hallucinates references 58% of the time, medical QA with 9% GPT-4 slips, creative writing with 30% factual drifts, or math reasoning where 1 in 5 answers is a hallucinated error, AI systems across tasks—from code docs (12% errors) to entity extraction (10% new made-up entities)—consistently mix truth with fiction, with even translation adding 7% false info and multi-hop reasoning inventing 19% of the facts.
This sentence balances conciseness with coverage, uses conversational phrasing ("gets... wrong," "hallucinates references," "mix truth with fiction"), and highlights both the range of tasks and the varying severity of hallucinations, all while maintaining a natural, human tone.
Reference
Cite This Report
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APA
Felix Zimmermann. (2026, February 24). AI Hallucinations Statistics. Gitnux. https://gitnux.org/ai-hallucinations-statistics
MLA
Felix Zimmermann. "AI Hallucinations Statistics." Gitnux, 24 Feb 2026, https://gitnux.org/ai-hallucinations-statistics.
Chicago
Felix Zimmermann. 2026. "AI Hallucinations Statistics." Gitnux. https://gitnux.org/ai-hallucinations-statistics.
Sources & references
15 datasets cited across this report · attribution is report-level