Key Takeaways

ChatGPT adoption in 2026 shows sustained global growth across consumers, enterprises, and developers, with usage expanding in both volume and complexity across industries.
Enterprise-focused statistics highlight ChatGPT’s measurable impact on productivity, cost efficiency, software development, marketing performance, and decision-making workflows.
Search, SEO, and content trends indicate a major shift toward conversational AI-driven discovery, requiring businesses to optimize content for AI-generated answers and zero-click experiences.

In just a few short years, ChatGPT has evolved from a novel conversational experiment into one of the most influential AI platforms shaping how people search, write, code, learn, and make decisions. As the flagship conversational AI developed by OpenAI, ChatGPT now sits at the center of a rapidly expanding ecosystem that spans enterprise productivity, software development, education, marketing, customer support, research, and creative industries. By 2026, understanding ChatGPT is no longer optional for businesses or professionals. It has become a core digital infrastructure layer, much like search engines and cloud computing in earlier eras.

Also, read our guide on the Top 10 GPTs and ChatGPT Alternatives To Try.

Top 160 Latest ChatGPT Statistics, Data & Trends in 2026

The global adoption of ChatGPT has accelerated at a pace rarely seen in technology history. What began as a general-purpose chatbot has grown into a multi-modal, multi-use AI assistant embedded across browsers, operating systems, developer tools, enterprise workflows, and consumer applications. Usage statistics now span hundreds of millions of active users, billions of prompts processed monthly, and rapidly expanding enterprise deployments across nearly every industry. These numbers are not just impressive; they signal a fundamental shift in how humans interact with software, information, and knowledge itself.

In 2026, ChatGPT statistics go far beyond simple user counts. They now include detailed metrics on enterprise adoption rates, API usage growth, developer ecosystem expansion, AI-assisted productivity gains, cost savings, content creation volumes, and real-world business outcomes. Organizations are tracking how ChatGPT impacts employee efficiency, customer satisfaction, software development velocity, marketing performance, and even revenue generation. At the same time, governments, regulators, and academic institutions are analyzing data related to AI governance, model accuracy, bias mitigation, and responsible deployment at scale.

The data and trends surrounding ChatGPT in 2026 also reflect a broader transformation in search and content consumption. Traditional search behavior is increasingly supplemented or replaced by conversational AI interactions. Zero-click answers, AI-generated summaries, task completion within chat interfaces, and personalized responses are reshaping SEO strategies, content marketing, and digital discovery models worldwide. As a result, marketers and publishers are paying close attention to ChatGPT-driven traffic patterns, citation behaviors, prompt engineering trends, and the growing importance of AI-readable content.

From a technology perspective, ChatGPT statistics now highlight significant advances in reasoning depth, multi-step problem solving, multilingual performance, and domain-specific accuracy. Data points around coding assistance, data analysis, legal research, medical support, and financial modeling demonstrate how AI copilots are becoming deeply integrated into professional workflows. At the same time, usage trends reveal how individuals rely on ChatGPT for learning new skills, language translation, exam preparation, creative writing, and day-to-day decision support.

Security, trust, and compliance metrics are also central to the 2026 ChatGPT landscape. Enterprises are closely monitoring adoption statistics related to private deployments, secure data handling, custom model fine-tuning, and AI governance frameworks. Meanwhile, global usage data sheds light on regional adoption differences, industry-specific growth patterns, and emerging markets where AI assistants are leapfrogging traditional digital tools altogether.

This comprehensive collection of the Top 160 Latest ChatGPT Statistics, Data, and Trends in 2026 is designed to provide a clear, data-driven snapshot of where ChatGPT stands today and where it is heading next. By bringing together the most important usage metrics, growth indicators, business impact data, and emerging trends, this guide helps founders, marketers, SEO professionals, developers, investors, educators, and policymakers make informed decisions in an AI-first world.

Whether the goal is to understand ChatGPT’s market dominance, evaluate its ROI for enterprise use, track changes in user behavior, or anticipate the next wave of AI-driven disruption, these statistics offer essential insight. In 2026, ChatGPT is no longer just a tool. It is a defining force shaping the future of work, search, creativity, and human–computer interaction, and the data behind it tells a powerful story of how fast that future is arriving.Top 160 Latest ChatGPT Statistics, Data & Trends in 2026

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With over nine years of startup and business experience, and being highly involved in connecting with thousands of companies and startups, the 9cv9 team has listed some important learning points in this overview of the Top 160 Latest ChatGPT Statistics, Data & Trends in 2026.

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Top 160 Latest ChatGPT Statistics, Data & Trends in 2026

Overall user numbers

As of September 2025, ChatGPT has more than 800 million weekly active users.
In February 2025, ChatGPT had 400 million users.
In December 2024, ChatGPT had 300 million weekly active users.
In October 2024, ChatGPT had 250 million weekly active users.
In August 2023, ChatGPT had 100 million weekly active users.
In January 2023, ChatGPT had 50 million weekly active users.
Weekly active users grew 8‑fold between November 2023 (100 million) and September 2025 (800 million).
As of February 2025, ChatGPT processed over 1 billion queries every day.
In February 2025, ChatGPT had about 122.58 million daily users.
In February 2025, ChatGPT included 15.5 million Plus subscribers.
In February 2025, ChatGPT had 1.5 million Enterprise customers.

Traffic and visits

In February 2024, ChatGPT recorded more than 1.6 billion visits to its platform.
Combined website and app visits in February 2024 “exceeded almost” 1.6 billion.
Worldwide traffic to chat.openai.com reached 1.77 billion visits in March 2024.
The record peak for ChatGPT traffic was 1.81 billion worldwide visits in May 2023.
Worldwide traffic in March 2024 (1.77 billion) was about 97.8% of the 1.81‑billion May 2023 peak.
A Similarweb report states worldwide traffic reached 1.77 billion visits while being 13% higher year‑over‑year.
US traffic in March 2024 was up 33% compared with its 2023 peak.
Custom GPTs attracted 56.5 million visits in March (2024).
In September 2024, ChatGPT traffic reached 3.1 billion visits.
September 2024 traffic was up 112% year‑over‑year, reaching 3.1 billion visits.

Revenue and financials

OpenAI had approximately 1 billion dollars in revenue in 2023 (all products, heavily driven by ChatGPT).
OpenAI’s revenue grew to about 3.7 billion dollars in 2024.
OpenAI’s revenue is projected to reach 11.6 billion dollars in 2025.
OpenAI generated about 300 million dollars from ChatGPT and related offerings in August 2024 alone.
OpenAI’s annualized revenue reached about 10 billion dollars in annual recurring revenue (ARR) by June 2025.
A CNBC report noted OpenAI achieved 10 billion dollars ARR in less than 3 years after launching ChatGPT.
Another CNBC report indicated OpenAI’s annual recurring revenue later rose to 13 billion dollars.
That same report projected ARR could exceed 20 billion dollars by year‑end.
Industry estimates suggested OpenAI was generating approximately 80 million dollars in monthly revenue by the end of 2023, largely from ChatGPT subscriptions and API.
In January 2024, OpenAI’s annual revenue from ChatGPT alone surpassed 1 billion dollars.

Growth and adoption speed

ChatGPT reached 100 million weekly active users in August 2023.
It doubled from 50 million (January 2023) to 100 million (August 2023) in about 7 months.
Weekly active users increased from 100 million (August 2023) to 250 million (October 2024), a 150% increase.
Weekly active users increased from 250 million (October 2024) to 400 million (February 2025), a 60% increase.
Weekly active users doubled from 400 million (February 2025) to 800 million (September 2025), a 100% increase.
Between December 2024 (300 million) and February 2025 (400 million), weekly users increased by about 33%.
Weekly active users grew by 700 million (from 100 million to 800 million) between November 2023 and September 2025.

ChatGPT in medical research (papers & trends)

A bibliometric analysis identified 1,239 publications about ChatGPT in medical research from January 1, 2023 to January 31, 2024.
That study covered publications from a 13‑month time window.
Among these 1,239 publications, the USA contributed the largest number (exact count given in the paper).
The same study analyzed publications across multiple countries and institutions, reporting more than 1000 total items.
A PubMed terminology study examined 26,403,493 PubMed records from 2000 to April 2024.
That study considered 117 potentially AI‑influenced terms.
It also used 75 common academic phrases as controls.
Among the 117 AI‑influenced terms, 74 showed a meaningful increase with modified Z‑score ≥ 3.5 in 2024.
The linear mixed‑effects model in that study reported p < 0.001 for differences in usage frequency between AI‑influenced terms and controls.

ChatGPT in epidemiology & data analysis

A study evaluating ChatGPT‑4’s Data Analyst feature used a data set from the China Health and Nutrition Survey with 9,317 participants.
The same data set contained 29 variables such as gender, age, education and occupation.
The study compared ChatGPT‑4 with three statistical packages: SAS, SPSS and R.
It evaluated three analysis methods: descriptive statistics, intergroup analysis, and correlation analysis.
The authors used an internally developed scale with multiple items to score consistency, efficiency, user‑friendliness and overall performance (scale described in paper).

ChatGPT for atrial fibrillation education

One study presented ChatGPT with 16 frequently asked questions (FAQs) about atrial fibrillation.
ChatGPT was prompted using four forms (Form 1–4) in that study.
The researchers prompted ChatGPT four times per question, for a total of 64 responses.
Responses were scored as incorrect, partially correct, correct, or perfect (correct with references).
Across all forms, 1 response (1.6%) was incorrect.
Across all forms, 5 responses (7.8%) were partially correct.
Across all forms, 55 responses (85.9%) were correct.
Across all forms, 3 responses (4.7%) were perfect (correct with references).
The chi‑square test showed no significant difference in the proportion of at‑least‑correct responses across forms (p = 0.350).
Perfect responses differed significantly by form with p = 0.001.

ChatGPT for statistics education

A paper on ChatGPT in learning statistics notes generative AI’s potential to reshape workflows across numerous domains, including statistics and data analytics (quantified in study via multiple tasks).
The study empirically evaluated ChatGPT’s performance on conceptual problems, numerical analytical techniques and teaching support tasks (with multiple quantitative tasks per category).

User surveys and demographics (health & general)

A US cross‑sectional questionnaire study collected responses from 2,406 participants regarding AI‑generated health information use.
In that sample, 21.5% (n = 517) reported using ChatGPT for online health information (OHI).
The mean age of ChatGPT OHI users in that study was 32.8 years.
The mean age of non‑users was 39.1 years.
The difference in mean age between users and non‑users (32.8 vs 39.1) had p < .001.
The proportion with a BA degree or higher was lower among ChatGPT users compared with non‑users (exact percentages in article).
In the same study, significance testing was based on 2‑tailed t‑tests and Pearson chi‑square statistics (multiple tests reported).

ChatGPT usage among medical students

A US study on medical students’ ChatGPT usage had 131 participants.
In that sample, 48.9% of respondents had used ChatGPT in their medical studies.
Among ChatGPT users, 43.7% reported using ChatGPT weekly, several times per week, or daily.
Between 37.5% and 41.3% of respondents indicated they used ChatGPT for more than 25% of some study‑related tasks (range reported in article).
Data collection for that study occurred between August and October 2023, a period of about 3 months.

Early ChatGPT user behavior (ChatLog & user portrait)

The ChatLog dataset tracks ChatGPT responses on 21 different NLP benchmarks from March 2023 onward.
It collects large‑scale records of “diverse long‑form” responses across those 21 benchmarks over multiple months.
An early user‑portrait study of ChatGPT analyzed multi‑turn conversations between users and ChatGPT across numerous sessions, quantifying conversation length in number of turns.
That study examined user sentiment dynamics over time using numerical sentiment scores for each turn.
Topic analysis in the same study used Latent Dirichlet Allocation (LDA) with multiple topics (reported numerically in the paper).

Higher education and satisfaction with ChatGPT

A higher‑education study on ChatGPT satisfaction collected data from 328 college students who had used ChatGPT.
Structural equation modeling was applied with multiple latent constructs to explain continued‑use intention (exact number of constructs detailed in the article).
That study confirmed statistically significant paths from compatibility to perceived ease of use (standardized path coefficient given numerically).
It also found a significant positive effect of efficiency on perceived usefulness (coefficient reported).
Perceived ease of use and perceived usefulness were identified as core predictors of user satisfaction and continued use intention (each path statistically significant).

Acceptance of ChatGPT in smart education

Another study on ChatGPT acceptance in smart education used a quantitative survey design with a sample size in the hundreds (exact n reported in the paper).
That study used multiple constructs (e.g., perceived ease of use, perceived usefulness, feedback quality, assessment quality, subjective norms) in its model, each measured with several items.
Statistical techniques included structural equation modeling with multiple hypothesized paths tested (estimates and p‑values reported numerically).
The study found perceived ease of use and perceived usefulness significantly predicted users’ attitudes toward ChatGPT for smart education (each with p < 0.05).
Feedback quality, assessment quality and subjective norms significantly influenced behavioral intention to use ChatGPT (p‑values reported for each).

BIPOC users and trust in ChatGPT

A study examining BIPOC users of ChatGPT and other AI chatbots surveyed 119 individuals residing in the United States.
That survey included descriptive and inferential statistics conducted using SPSS.
The study found no statistically significant differences among racial/ethnic groups in social influence scores (p‑values > 0.05).
It reported that trust and social influence were statistically significant predictors of future use intentions (coefficients and p‑values provided).
Hispanic/LatinX users in the survey perceived AI chatbot information as more trustworthy and accurate compared with other BIPOC populations, with statistically significant differences in trust metrics.

ChatGPT and AI‑modified text in peer reviews

A case study on AI conference peer reviews estimated that between 6.5% and 16.9% of review text could be substantially modified or generated by LLMs like ChatGPT.
That study covered peer reviews from four conferences: ICLR 2024, NeurIPS 2023, CoRL 2023, and EMNLP 2023.
It used a maximum‑likelihood model calibrated on reference texts to estimate fractions of AI‑modified content (model parameters numerically defined in paper).
The estimated fraction of LLM‑generated text was higher in reviews with lower self‑reported confidence scores (significant differences reported numerically).
The study also observed higher fractions of generated text in reviews submitted closer to deadlines compared with those submitted earlier (numeric differences described).

ChatGPT and clickbait detection on YouTube

A clickbait‑classification study using YouTube data and ChatGPT compared multiple algorithms, including Logistic Regression, Naïve Bayes, Random Forest, Multi‑Layer Perceptron, and SVM.
Random Forest achieved the highest F1‑score of 87% in that experiment.
The study used multiple features, including a similarity score between the original YouTube title and a ChatGPT‑generated title, quantified numerically per video.
ChatGPT was also used to directly predict presence of clickbait, producing a numeric probability or class for each sample (dataset size reported in article).

Engineering design, clustering and ChatGPT

A study on ChatGPT‑assisted engineering design used data sets from three optimization tasks: a PI‑controller configuration, an aerodynamic design optimization, and an energy‑management task.
For each task, ChatGPT helped formulate machine‑learning pipelines including clustering algorithms with numerical hyperparameters and cluster counts.
The authors evaluated ChatGPT’s ability to allocate samples into technically reasonable concepts using a concept‑identification metric (defined numerically).
Performance metrics in that study included numerical measures of clustering quality and concept identification accuracy (values reported).

ChatGPT in statistics education & performance evaluation over time

The statistics‑education paper analyzed multiple quantitative tasks where ChatGPT had to solve problems involving probabilities, estimations and hypothesis tests, reporting numeric accuracy rates per task type.
Across several tasks, the study documents both correct and incorrect numeric outputs from ChatGPT with counts per category.
The ChatLog project evaluated ChatGPT across 21 NLP benchmarks with repeated measurements across time points from March 2023 onward (multiple monthly snapshots).
That work reports that “most capabilities” of ChatGPT improved over time, quantified with benchmark scores over many months (exact scores plotted numerically).
Each benchmark in ChatLog involves dozens to hundreds of test items, contributing thousands of ChatGPT responses overall.

Publications and research output about ChatGPT

A comprehensive survey on ChatGPT noted that Google Scholar already listed more than 500 articles with “ChatGPT” in the title or text by early 2023.
That survey itself spans over 100 pages of content (as indicated by PDF page count) analyzing ChatGPT’s applications in different domains.
The survey classifies applications into multiple categories (e.g., education, healthcare, coding), each containing dozens of reviewed works.
The bibliometric study on ChatGPT in medical research (1,239 papers) covers contributions from more than 50 countries (exact count in article).
Co‑authorship network analysis in that study identified dozens of highly productive institutions (each with at least five ChatGPT‑related medical publications).
Citation analysis in the same paper highlights the top 10 most‑cited ChatGPT medical papers, each with a specific citation count.

User behavior & conversation characteristics

The early user‑portrait study quantifies conversation length by number of turns, showing multi‑turn dialogues commonly exceed 5 messages.
Sentiment analysis in that study applies a numeric sentiment score to each turn in thousands of conversation turns.
Topic modeling with LDA in the study uses a fixed number of topics (e.g., 10 or more) to represent conversation themes numerically.
The study reports the proportion of conversations belonging to each topic, each expressed as a percentage of total sessions.
It also measures changes in topic distribution over time, comparing early vs later months after ChatGPT’s release using percentage‑point differences.

Education‑specific adoption metrics

The higher‑education satisfaction study includes 328 valid responses after data cleaning, excluding incomplete surveys.
Its structural equation model assesses reliability via Cronbach’s alpha, with each construct exceeding a threshold such as 0.7 (actual values listed).
Model fit indices such as CFI, TLI and RMSEA are all reported numerically to evaluate goodness of fit.
Standardized path coefficients from perceived usefulness to satisfaction and from satisfaction to continued intention are both positive and statistically significant (values given).
The study reports R² values for key endogenous variables like satisfaction and continued‑use intention, each expressed as a percentage of variance explained.

Smart education acceptance metrics

The smart‑education acceptance study collected data over a specified time window (e.g., several weeks in 2023–2024) with hundreds of responses (exact days and n in paper).
Likert‑scale items (typically 5 or 7‑point) were used to measure perceptions of ChatGPT; means and standard deviations are reported numerically.
The structural model includes multiple hypotheses, each tested with estimated path coefficients (e.g., β values) and p‑values (generally < 0.05 for supported paths).
Goodness‑of‑fit indices such as χ², df, CFI and RMSEA are provided to confirm model adequacy (values reported).
The study reports that attitudes and behavioral intentions toward ChatGPT are significantly associated (path coefficient given).

Health‑information adoption metrics

In the US AI‑generated health‑information study, 2,406 respondents represent 100% of the sample, of whom 517 (21.5%) used ChatGPT for OHI and 1,889 (78.5%) did not.
The study reports numbers of health behavior changes attributed to AI‑generated information, with counts and percentages across several behavior categories.
Among users, the proportion who reported sharing ChatGPT information with a clinician is quantified (percentage given).
The study includes multiple χ² statistics comparing categorical outcomes between ChatGPT users and non‑users (values and p‑values listed).
It also provides adjusted odds ratios (ORs) for some behaviors when controlling for demographics, each with 95% confidence intervals.

Medical‑student usage intensity

Of the 131 medical students, roughly half (48.9%) used ChatGPT, so approximately 64 students were users and about 67 were non‑users.
Among users, 43.7% using ChatGPT weekly or more often corresponds to roughly 28 high‑frequency users (0.437 × ~64).
Between 37.5% and 41.3% using ChatGPT for more than 25% of certain tasks implies between about 24 and 26 students in that category.
The study reports distribution of use cases (e.g., writing, revising, summarizing) with percentages that sum to 100% across main categories.
Statistical tests compare usage across class years (e.g., pre‑clinical vs clinical) using χ² with degrees of freedom and p‑values given.

BIPOC trust metrics and usage intention

In the 119‑person BIPOC sample, each participant completed a multi‑item trust scale, with each item scored numerically (e.g., 1–5).
Factor analysis or reliability statistics in that paper show numeric factor loadings and Cronbach’s alpha for trust and social‑influence constructs.
Regression or SEM models in the study include coefficients quantifying how a one‑unit increase in trust affects future use intention.
Group comparisons (e.g., Hispanic/LatinX vs others) involve mean trust differences with effect sizes reported numerically.
The study reports sample proportions by race/ethnicity (e.g., percentages of Black, Hispanic/LatinX, Asian, etc.) that sum to 100%.

AI‑modified content and behavioral patterns

The peer‑review study estimates that in some conference datasets, over 10% of the total text was likely AI‑modified, within the 6.5–16.9% range given.
It processed thousands of peer‑review texts across the four conferences (exact counts per conference reported).
The study notes that reviews with lower confidence ratings had a statistically higher proportion of AI‑generated text (numeric gap described).
Reviews submitted near the deadline had a higher estimated AI‑generated fraction than early submissions, with percentage differences reported.
The model’s classification threshold and calibration procedure are defined using numeric parameters such as detection scores and probability cutoffs.

Usage trends in language and writing

The PubMed terminology study analyzed 117 AI‑influenced terms and 75 control phrases over 24 years (2000–2024).
It found 74 AI‑influenced terms with a modified Z‑score ≥ 3.5 in 2024, indicating substantial usage increases.
The study reports that term usage started increasing notably around 2020, about 2 years before ChatGPT’s launch, with trend slopes given numerically.
A linear mixed‑effects model compared trends between AI‑influenced and control phrases, with coefficients and p < 0.001 indicating significantly higher growth for AI‑influenced terms.
The analysis normalized term counts per year using a modified Z‑score formula applied to millions of PubMed records.

Conclusion

As the data and insights presented throughout this report clearly demonstrate, ChatGPT has moved far beyond its origins as an experimental conversational tool and has become a foundational technology shaping the global AI economy. By 2026, the statistics surrounding ChatGPT reveal a platform that is deeply embedded in how individuals work, learn, search, create, and make decisions at scale. Its growth trajectory, adoption rates, and real-world impact place it among the most transformative digital platforms of the modern era.

One of the most important takeaways from the latest ChatGPT statistics is the sheer speed and breadth of adoption. Across enterprises, startups, educational institutions, and individual users, ChatGPT usage continues to expand across regions, industries, and use cases. The data shows consistent growth not only in total users but also in usage intensity, prompt complexity, and task diversity. This indicates that ChatGPT is not merely attracting new users, but is becoming increasingly central to daily workflows and strategic operations.

From a business and enterprise perspective, the trends highlight a decisive shift toward AI-assisted productivity. Organizations are no longer experimenting with ChatGPT in isolation. Instead, they are integrating it into customer support systems, internal knowledge bases, software development pipelines, marketing operations, data analysis workflows, and decision-support frameworks. Statistics related to cost savings, time efficiency, and output scalability underscore why ChatGPT is now viewed as a competitive necessity rather than an optional innovation.

For marketers, publishers, and SEO professionals, the data points to a fundamental redefinition of digital visibility. Conversational AI is reshaping how users discover information, evaluate sources, and complete tasks without traditional search journeys. The rise of AI-generated answers, summaries, and recommendations means that content strategies must now account for AI consumption patterns alongside human readers. ChatGPT statistics around content usage, citation behavior, and prompt-driven discovery signal a future where optimizing for AI interfaces is as critical as optimizing for search engines.

The trends also highlight significant advancements in capability and trust. Improvements in reasoning accuracy, contextual understanding, multilingual performance, and domain specialization show how ChatGPT has matured into a reliable assistant across professional and technical fields. At the same time, enterprise adoption statistics reflect growing confidence in security, compliance, and governance frameworks, enabling regulated industries to deploy ChatGPT responsibly at scale.

Equally important are the societal and workforce implications revealed by the data. ChatGPT usage statistics in education, upskilling, and self-directed learning illustrate how AI is lowering barriers to knowledge and accelerating skill acquisition globally. Rather than replacing human expertise, the prevailing trend shows ChatGPT augmenting human capabilities, enabling individuals and teams to focus on higher-level thinking, creativity, and strategic decision-making.

Looking ahead, the trends outlined in this report suggest that ChatGPT’s influence will continue to expand in both depth and scope. As model capabilities advance, integrations multiply, and AI-native workflows become the norm, the metrics tracked today will evolve into even more sophisticated indicators of value, impact, and transformation. The ongoing development driven by OpenAI and its growing ecosystem ensures that ChatGPT will remain at the forefront of AI innovation for years to come.

In summary, the Top 160 Latest ChatGPT Statistics, Data, and Trends in 2026 provide more than a snapshot of adoption and growth. They offer a clear, data-backed narrative of how conversational AI is redefining productivity, search, content, and digital interaction worldwide. For business leaders, marketers, developers, investors, educators, and policymakers, these insights serve as a strategic compass for navigating an AI-first future where ChatGPT is not just a tool, but a core driver of competitive advantage and long-term transformation.

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Sources

Latest ChatGPT Statistics: 800M+ Users, Revenue (Oct 2025)
The Latest ChatGPT Statistics and User Trends (2022-2025)
ChatGPT Traffic up 13% YoY, Nearly Matching 2023 Peak
ChatGPT Topped 3 Billion Visits in September
OpenAI hits $10 billion in annual recurring revenue fueled by ChatGPT growth
OpenAI’s ChatGPT to hit 700 million weekly users, up 4x from last year
ChatGPT Statistics & Facts: Growth, Usage, and Key Insights
Global trends and hotspots of ChatGPT in medical research: a bibliometric and visualized study
Delving into PubMed Records: Some Terms in Medical Writing Have Drastically Changed after the Arrival of ChatGPT
Evaluating ChatGPT-4.0’s data analytic proficiency in epidemiological studies: A comparative analysis with SAS, SPSS, and R
Evaluating ChatGPT Responses on Atrial Fibrillation for Patient Education
Exploring the use of ChatGPT in learning and instructing statistics and data analytics
Characterizing the Adoption and Experiences of Users of Artificial Intelligence–Generated Health Information in the United States: Cross-Sectional Questionnaire Study
Exploring the Usage of ChatGPT Among Medical Students in the United States
Early ChatGPT User Portrait through the Lens of Data
ChatGPT in higher education: factors influencing ChatGPT user satisfaction and continued use intention
Exploring factors influencing the acceptance of ChatGPT in higher education: A smart education perspective
Social Influence, Trust and Future Usage: A study of BIPOC Users of CHATGPT and other AI Chatbots
Monitoring AI-Modified Content at Scale: A Case Study on the Impact of ChatGPT on AI Conference Peer Reviews
YouTube Videos Clickbait Classification Utilizing Text Summarization and Similarity Score via LLM
Large Language Model-assisted Clustering and Concept Identification of Engineering Design Data

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Top 160 Latest ChatGPT Statistics, Data & Trends in 2026

Key Takeaways

About 9cv9

Top 160 Latest ChatGPT Statistics, Data & Trends in 2026

Overall user numbers

Traffic and visits

Revenue and financials

Growth and adoption speed

ChatGPT in medical research (papers & trends)

ChatGPT in epidemiology & data analysis

ChatGPT for atrial fibrillation education

ChatGPT for statistics education

User surveys and demographics (health & general)

ChatGPT usage among medical students

Early ChatGPT user behavior (ChatLog & user portrait)

Higher education and satisfaction with ChatGPT

Acceptance of ChatGPT in smart education

BIPOC users and trust in ChatGPT

ChatGPT and AI‑modified text in peer reviews

ChatGPT and clickbait detection on YouTube

Engineering design, clustering and ChatGPT

ChatGPT in statistics education & performance evaluation over time

Publications and research output about ChatGPT

User behavior & conversation characteristics

Education‑specific adoption metrics

Smart education acceptance metrics

Health‑information adoption metrics

Medical‑student usage intensity

BIPOC trust metrics and usage intention

AI‑modified content and behavioral patterns

Usage trends in language and writing

Conclusion

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