Which AI Models Are Leading the Way in Reducing Hallucinations and Improving Accuracy?

AI models are helping us in a lot of areas but they tend to hallucinate too and give us inaccurate information. IBM defines hallucinations in AI chatbots or computer vision tools as some outputs that come out as inaccurate due to detection of some patterns that do not exist. Vectara analyzed 1,000 short documents with each LLMs to detect hallucinations in them and came up with top 15 large language models with the lowest rates of hallucination. According to the data, Zhipu AI’s GLM-4-9B-Chat has the least hallucination rate at 1.3%. Google Gemini-2.0-Flash-Esp has the second lowest hallucination rate at 1.3% as well.

The top third LLM with least hallucination levels is OpenAI’s o1-mini with 1.4% hallucination rate. With a hallucination rate of 1.5%, GPT-4o is the fourth model with least hallucination. GPT-4o-mini and GPT-4-Turbo have hallucination rates of 1.7%. It was observed that more specialized and smaller models have the lowest hallucination rates. OpenAI’s GPT-4 has a hallucination rate of 1.8%, while GPT-3.5-Turbo has a hallucination rate of 1.9%.

It is important for AI systems to show low levels of hallucination for them to work properly, especially in high-stake applications in healthcare, finance and law. Smaller models are slowly reducing hallucinations in their AI models, with Mistral 8×7B models reducing hallucinations in their AI generated texts.

Model	Hallucination Rate	Factual Consistency Rate	Answer Rate	Average Summary Length (Words)
Zhipu AI GLM-4-9B-Chat	1.3 %	98.7 %	100.0 %	58.1
Google Gemini-2.0-Flash-Exp	1.3 %	98.7 %	99.9 %	60
OpenAI-o1-mini	1.4 %	98.6 %	100.0 %	78.3
GPT-4o	1.5 %	98.5 %	100.0 %	77.8
GPT-4o-mini	1.7 %	98.3 %	100.0 %	76.3
GPT-4-Turbo	1.7 %	98.3 %	100.0 %	86.2
GPT-4	1.8 %	98.2 %	100.0 %	81.1
GPT-3.5-Turbo	1.9 %	98.1 %	99.6 %	84.1
DeepSeek-V2.5	2.4 %	97.6 %	100.0 %	83.2
Microsoft Orca-2-13b	2.5 %	97.5 %	100.0 %	66.2
Microsoft Phi-3.5-MoE-instruct	2.5 %	97.5 %	96.3 %	69.7
Intel Neural-Chat-7B-v3-3	2.6 %	97.4 %	100.0 %	60.7
Qwen2.5-7B-Instruct	2.8 %	97.2 %	100.0 %	71
AI21 Jamba-1.5-Mini	2.9 %	97.1 %	95.6 %	74.5
Snowflake-Arctic-Instruct	3.0 %	97.0 %	100.0 %	68.7
Qwen2.5-32B-Instruct	3.0 %	97.0 %	100.0 %	67.9
Microsoft Phi-3-mini-128k-instruct	3.1 %	96.9 %	100.0 %	60.1
OpenAI-o1-preview	3.3 %	96.7 %	100.0 %	119.3
Google Gemini-1.5-Flash-002	3.4 %	96.6 %	99.9 %	59.4
01-AI Yi-1.5-34B-Chat	3.7 %	96.3 %	100.0 %	83.7
Llama-3.1-405B-Instruct	3.9 %	96.1 %	99.6 %	85.7
Microsoft Phi-3-mini-4k-instruct	4.0 %	96.0 %	100.0 %	86.8
Llama-3.3-70B-Instruct	4.0 %	96.0 %	100.0 %	85.3
Microsoft Phi-3.5-mini-instruct	4.1 %	95.9 %	100.0 %	75
Mistral-Large2	4.1 %	95.9 %	100.0 %	77.4
Llama-3-70B-Chat-hf	4.1 %	95.9 %	99.2 %	68.5
Qwen2-VL-7B-Instruct	4.2 %	95.8 %	100.0 %	73.9
Qwen2.5-14B-Instruct	4.2 %	95.8 %	100.0 %	74.8
Qwen2.5-72B-Instruct	4.3 %	95.7 %	100.0 %	80
Llama-3.2-90B-Vision-Instruct	4.3 %	95.7 %	100.0 %	79.8
XAI Grok	4.6 %	95.4 %	100.0 %	91
Anthropic Claude-3-5-sonnet	4.6 %	95.4 %	100.0 %	95.9
Qwen2-72B-Instruct	4.7 %	95.3 %	100.0 %	100.1
Mixtral-8x22B-Instruct-v0.1	4.7 %	95.3 %	99.9 %	92
Anthropic Claude-3-5-haiku	4.9 %	95.1 %	100.0 %	92.9
01-AI Yi-1.5-9B-Chat	4.9 %	95.1 %	100.0 %	85.7
Cohere Command-R	4.9 %	95.1 %	100.0 %	68.7
Llama-3.1-70B-Instruct	5.0 %	95.0 %	100.0 %	79.6
Llama-3.1-8B-Instruct	5.4 %	94.6 %	100.0 %	71
Cohere Command-R-Plus	5.4 %	94.6 %	100.0 %	68.4
Llama-3.2-11B-Vision-Instruct	5.5 %	94.5 %	100.0 %	67.3
Llama-2-70B-Chat-hf	5.9 %	94.1 %	99.9 %	84.9
IBM Granite-3.0-8B-Instruct	6.5 %	93.5 %	100.0 %	74.2
Google Gemini-1.5-Pro-002	6.6 %	93.7 %	99.9 %	62
Google Gemini-1.5-Flash	6.6 %	93.4 %	99.9 %	63.3
Microsoft phi-2	6.7 %	93.3 %	91.5 %	80.8
Google Gemma-2-2B-it	7.0 %	93.0 %	100.0 %	62.2
Qwen2.5-3B-Instruct	7.0 %	93.0 %	100.0 %	70.4
Llama-3-8B-Chat-hf	7.4 %	92.6 %	99.8 %	79.7
Google Gemini-Pro	7.7 %	92.3 %	98.4 %	89.5
01-AI Yi-1.5-6B-Chat	7.9 %	92.1 %	100.0 %	98.9
Llama-3.2-3B-Instruct	7.9 %	92.1 %	100.0 %	72.2
databricks dbrx-instruct	8.3 %	91.7 %	100.0 %	85.9
Qwen2-VL-2B-Instruct	8.3 %	91.7 %	100.0 %	81.8
Cohere Aya Expanse 32B	8.5 %	91.5 %	99.9 %	81.9
IBM Granite-3.0-2B-Instruct	8.8 %	91.2 %	100.0 %	81.6
Mistral-7B-Instruct-v0.3	9.5 %	90.5 %	100.0 %	98.4
Google Gemini-1.5-Pro	9.1 %	90.9 %	99.8 %	61.6
Anthropic Claude-3-opus	10.1 %	89.9 %	95.5 %	92.1
Google Gemma-2-9B-it	10.1 %	89.9 %	100.0 %	70.2
Llama-2-13B-Chat-hf	10.5 %	89.5 %	99.8 %	82.1
AllenAI-OLMo-2-13B-Instruct	10.8 %	89.2 %	100.0 %	82
AllenAI-OLMo-2-7B-Instruct	11.1 %	88.9 %	100.0 %	112.6
Mistral-Nemo-Instruct	11.2 %	88.8 %	100.0 %	69.9
Llama-2-7B-Chat-hf	11.3 %	88.7 %	99.6 %	119.9
Microsoft WizardLM-2-8x22B	11.7 %	88.3 %	99.9 %	140.8
Cohere Aya Expanse 8B	12.2 %	87.8 %	99.9 %	83.9
Amazon Titan-Express	13.5 %	86.5 %	99.5 %	98.4
Google PaLM-2	14.1 %	85.9 %	99.8 %	86.6
Google Gemma-7B-it	14.8 %	85.2 %	100.0 %	113
Qwen2.5-1.5B-Instruct	15.8 %	84.2 %	100.0 %	70.7
Qwen-QwQ-32B-Preview	16.1 %	83.9 %	100.0 %	201.5
Anthropic Claude-3-sonnet	16.3 %	83.7 %	100.0 %	108.5
Google Gemma-1.1-7B-it	17.0 %	83.0 %	100.0 %	64.3
Anthropic Claude-2	17.4 %	82.6 %	99.3 %	87.5
Google Flan-T5-large	18.3 %	81.7 %	99.3 %	20.9
Mixtral-8x7B-Instruct-v0.1	20.1 %	79.9 %	99.9 %	90.7
Llama-3.2-1B-Instruct	20.7 %	79.3 %	100.0 %	71.5
Apple OpenELM-3B-Instruct	24.8 %	75.2 %	99.3 %	47.2
Qwen2.5-0.5B-Instruct	25.2 %	74.8 %	100.0 %	72.6
Google Gemma-1.1-2B-it	27.8 %	72.2 %	100.0 %	66.8
TII falcon-7B-instruct	29.9 %	70.1 %	90.0 %	75.5

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by Arooj Ahmed via Digital Information World