Editing Grok chatbot (section)

===Accuracy===
[[File:Grok-3 DeepSearch example.png|thumb|An example of Grok's DeepSearch feature, where it reasons and searches multiple sources before responding]]

Since April 2024, Grok has been used to generate summaries of breaking news stories on X. When a large number of verified users began to spread [[fake news|false stories]] about Iran having attacked Israel on April 4 (nine days before the [[April 2024 Iranian strikes against Israel|2024 Iranian strikes in Israel]]), Grok treated the story as real and created a headline and paragraph-long description of the event.<ref name="mashable" /> Days later it misunderstood many users joking about the [[Solar eclipse of April 8, 2024|solar eclipse]] with the summarized headline "Sun's Odd Behavior: Experts Baffled".<ref>{{cite news |last1=Novak |first1=Matt |title=Elon Musk's Grok Creates Bizarre Fake News About the Solar Eclipse Thanks to Jokes on X |url=https://gizmodo.com/grok-ai-creates-bizarre-fake-news-about-the-solar-eclip-1851396186 |access-date=April 16, 2024 |work=Gizmodo |date=April 8, 2024 }}</ref>

In February 2025, Latenode compared Grok 3 and ChatGPT. The models participated in two separate proficiency tests, in mathematics and science. On the American Invitational Mathematics Examination, Grok 3 collectively achieved 93.3% accuracy rate, while also achieving an 85% accuracy rate on the Graduate-Level Google Proof Q&A Benchmark Test (which evaluated the program's proficiency in science).<ref>{{Cite web |title=ChatGPT vs Grok 3: Comprehensive Performance Comparison of Leading AI Models |url=https://latenode.com/blog/chatgpt-vs-grok-3-comprehensive-performance-comparison-of-leading-ai-models |access-date=April 28, 2025 |website=Latenode.com }}</ref>{{Unreliable source?|date=April 2025}}

In 2025, a study by Uri Samet discussed Grok's potential role in supporting fact-checking workflows. The article suggested that large language models like Grok can assist fact-checkers by aggregating relevant information, identifying preliminary verification paths, and helping to mitigate certain biases, while emphasizing that final evaluations should remain with human reviewers.<ref>{{cite journal|author=Uri Samet|title=The positive influence of large language models on fact-checking practices: A case study of Grok|journal=World Journal of Advanced Engineering Technology and Sciences|volume=15|issue=3|date=2025|doi=10.30574/wjaets.2025.15.3.1123|pages=1727–1738|url=https://journalwjaets.com/node/1128|doi-access=free}}</ref>