Gemini is the latest iteration of Google’s AI technology. This solution has become a valuable tool for tackling modern business challenges. Use cases include marketing, data analysis, idea generation, report generation, etc.
As of mid-2026, Gemini’s lineup spans the earlier Gemini 2.5 and Gemini 3 families, with the newest Gemini 3.5 generation now led by Gemini 3.5 Flash as the default model.
In this article, you’ll find out the detailed Google Gemini statistics. We’ll highlight its strengths and compare its features to those of other leading language models. Let’s explore the numbers to understand how this solution stands out in the AI competition.
Below are the most notable Google Gemini stats:
Let’s go over the key details of this language model launched by Google:
Google Gemini AI release date
Google Gemini: December 6, 2023
Bard (former version): March 21, 2023
Parent company
Google, under Alphabet Inc.
Available regions
Over 230 countries and territories
Language support
Over 40 languages, including Chinese, Korean, Arabic, Hindi, and Spanish
Model variants
Gemini 3.5 Flash: the current default, combining frontier intelligence with speed and grounding.
Gemini 3.5 Pro: for frontier reasoning, coding, and long-context work.
Gemini 3.1 Pro: multimodal understanding and agentic tasks
Supported data types for input
Text, Image, Video, Audio, PDF
Knowledge cutoff
January 2025 (Gemini 3 series)
Supported # tokens for input
1M (up to 2M on Gemini 3.5 Pro)
Websites Like Gemini
ChatGPT, Claude, Grok, DeepSeek
Source: Google AI for Developers, Google DeepMind
Now that we got to know what Google Gemini is, let’s move into the usage stats.
Google Gemini attracts millions of users globally. Let’s review its performance in several key usage trends.
Gemini had 2.9 billion total monthly visits in May 2026
In May 2026, Google Gemini statistics showed 2.135 billion visits on desktop and 767 million visits on mobile, reaching 2.9 billion total. Gemini’s traffic has grown sharply through 2026, more than doubling its late-2025 levels.
Mar 2026
1.9B
693M
2.59B
Apr 2026
2.035B
726M
2.761B
May 2026
2.135B
767M
2.9B
+11.74%
In the Claude vs Google Gemini vs ChatGPT comparison, the latter still dominates in total visits. In May 2026, ChatGPT had 5.565 billion total visits, outpacing Google Gemini’s 2.903 billion, while Claude recorded 952.5 million.
Google Gemini attracted 334.5 million unique visitors in May 2026
Unique visitors to Gemini rose steadily through 2026, climbing from 288 million in March to 334.5 million by May. Desktop unique visitors grew from 161.8 million to 180.5 million over the period, while mobile unique visitors rose from 126.2 million to 153.9 million.
Google Gemini’s bounce rate of 28.05% is one of the lowest among competitors
Google Gemini statistics by month show a relatively stable bounce rate with slight fluctuations. As of May 2026, Google Gemini’s bounce rate stands at 28.05%, lower than ChatGPT’s 32.79% and just behind Claude’s 26.42%. A low bounce rate signals that visitors engage with the platform rather than leaving after a single page.
Gemini users viewed an average of 4.59 pages per visit in May 2026
Engagement on Google Gemini is evident in the pages-per-visit metric. Mobile users are significantly more engaged than desktop users, viewing roughly twice as many pages per session. Desktop users averaged 3.48 pages per visit in May 2026, while mobile users reached 7.67.
Mar 2026
3.40
5.88
Apr 2026
3.39
5.86
May 2026
3.48
7.67
The average visit duration for Gemini was 6 minutes and 59 seconds in May 2026
Desktop users spent an average of 5 minutes and 57 seconds per visit in May 2026, while mobile users stayed longer at 9 minutes and 55 seconds.
Mar 2026
5:54
10:50
Apr 2026
5:58
10:35
May 2026
5:57
9:55
Google Gemini shows improved engagement, but ChatGPT is a leader
According to the latest Google Gemini statistics as of May 2026, the platform holds strong engagement metrics. Google Gemini averages 4.59 pages per visit, surpassing both ChatGPT (4.19) and Claude (4.51). Gemini also leads in average visit duration at 6 minutes 59 seconds, compared to ChatGPT’s 5 minutes 58 seconds and Claude’s 6 minutes 2 seconds.
Gemini’s bounce rate of 28.05% performs better than ChatGPT (32.79%) but sits just behind Claude (26.42%). In terms of total traffic, ChatGPT continues to lead by a significant margin with 5.565 billion monthly visits, followed by Gemini at 2.903 billion and Claude at 952.5 million.
Monthly visits
2.903B
5.565B
952.5M
Monthly unique visitors
334.5M
510.2M
101.1M
Visits / Unique visitors
8.68
10.91
9.42
Visit duration
0:06:59
0:05:58
0:06:02
Pages per visit
4.59
4.19
4.51
Bounce rate
28.05%
32.79%
26.42%
Page views
13.31B
23.29B
4.296B
The Gemini app reached 750 million monthly active users in February 2026
Web traffic tells only part of the story, since the Gemini mobile app and Google’s broader AI surfaces reach a far larger audience. The Gemini app reached 750 million monthly active users by February 2026, up from 450 million earlier in the year, making it one of the fastest-growing AI platforms. Since its May 2024 launch, the app has been downloaded more than 430 million times, with around 27 million downloads in February 2026 alone.
Gemini’s reach extends well beyond the standalone app. Google’s AI Overviews now reach 2 billion monthly users, and AI Mode in Search has grown to 75 million daily active users. Taken together, these surfaces put Gemini in front of a larger audience than its web-traffic numbers suggest.
40% of users utilize Google Gemini for research purposes
Additionally, 30% of the respondents reported using this language model for creative endeavors. Some use cases include writing poems, scripts, and stories.
Furthermore, 20% indicated that they use Google Gemini for productivity. The examples encompass work or school projects. The remaining 10% of users engage with this language model for entertainment. For instance, they use it to play games and search for videos and music.
Source: SimilarWeb, My Learning
Google Gemini’s traffic distribution shows significant engagement. It spans across various regions, age groups, and marketing channels.
The United States was the top traffic source for Google Gemini in May 2026
As of May 2026, the United States led the traffic sources for Google Gemini with 12.43%. Japan followed with 6.19%, India with 5.56%, Korea with 5.39%, and Brazil with 5.18%. The rest of the world contributed 65.25% of the traffic.
The largest age group of Google Gemini users is 25-34. It makes up 28.56% of the audience
Google Gemini statistics show that the 25-34 age group is the most significant demographic. It comprises 28.58% of the audience. The 18-24 age group follows with 22.9%, while the 35-44 group accounts for 19.57%. Smaller segments include the 45-54 group at 14.2%, the 55-64 group at 9.29%, and users aged 65 and above at 5.47%.
Male users represent 57.61% of Google Gemini’s audience
Gender distribution data indicates that male users dominate the platform. This segment made up 57.61% of the audience. Meanwhile, the female counterparts constitute 42.39%.
Direct traffic is the leading channel for Google Gemini at 64.66%
Direct traffic accounts for 64.66% of Google Gemini’s traffic from marketing channels. The second-largest source is organic search at 16.77%, followed by organic social at 5.61%.
Direct
64.66%
Organic Search
16.77%
Organic Social
5.61%
Display
3.27%
Paid Search
3.22%
Referrals
3.10%
Gen AI
1.24%
1.18%
Paid Social
0.93%
Affiliates
0.01%
A growing signal is the Gen AI channel at 1.24%, which captures users arriving from other AI tools.
Source: SimilarWeb
Let’s explore Google Gemini’s cost structure and compare it with other language models.
Gemini 3.5 Flash offers cost-effective performance among advanced AI models, with $1.50 (input) and $9 (output) per million tokens
Artificial Analysis has carried out research regarding input and output prices. The former implies the cost per token included in the request sent to the API, and the latter the cost per token returned from the API.
Google Gemini statistics indicate that the Gemini 3.5 Flash value is $1.50 per million input or $9 per million output tokens. While that is a step up from the earlier Gemini 3 Flash ($0.50 / $3), it remains competitive for its intelligence tier.
For comparison, GPT-5.5 costs $5 per million input and $30 per million output tokens, while Claude Opus 4.8 costs $5 input and $25 output. On the lower end, Grok 4.3 costs just $1.25 input and $2.50 output per million tokens.
Grok 4.3
$1.25
$2.50
Gemini 3.5 Flash
$1.50
$9
Claude Opus 4.8
$5
$25
GPT-5.5
$5
$30
Source: Artificial Analysis
Is Google Gemini better than ChatGPT? How does it compare to other language models regarding performance and capabilities? To answer these questions, let’s review Google Gemini statistics below. We’ll focus on the training data, context window, and benchmark performances.
The LaMDA model used in the former version, Google Bard, was trained on the Infinite dataset containing 1.56 trillion words and 137 billion parameters
This massive dataset only required 750 GB of storage. It comprises 12.5% of C4-based data and an equal percentage of code documents from programming tutorials, Q&A websites, and others. Additionally, it includes 6.5% of English web documents and 6.5% of non-English web documents.
Gemini 3.5 Flash features a context window of up to one million tokens
Gemini 3.5 Flash and Gemini 3.1 Pro both feature a context window of up to one million tokens, matching Claude Opus 4.8. This positions them among the leaders in long-context processing. Gemini 3.5 Pro extends this further to two million tokens, alongside Grok 4 Fast at the same level.
These models enable near-perfect recall on long-context retrieval tasks across multiple formats, including long documents, lines of code, audio, video, and more.
Comparing the Artificial Analysis Intelligence Index, Gemini 3.5 Flash scores 50
Artificial Analysis runs an evaluation incorporating nine tests spanning reasoning, knowledge, math, and coding. On this index (version 4.1, which rescaled earlier scores), Gemini 3.5 Flash scores 50, placing it ninth among 155 models and well above the average of 29. Gemini 3.1 Pro scores 46. Let’s review the data in more detail.
Claude Opus 4.8
56
GPT-5.5
55
Gemini 3.5 Flash
50
Gemini 3.1 Pro
46
Grok 4.3
38
Gemini 3.5 Flash processes 183.9 output tokens per second
Google Gemini statistics show that Gemini 3.5 Flash is among the fastest leading models, processing 183.9 tokens per second, ahead of Grok 4.3 at 164.9 tokens per second.
Gemini 3.5 Flash leads multimodal understanding among current models
According to the latest benchmarks, Gemini 3.5 Flash demonstrates competitive capabilities across multiple evaluation categories, and it tops the field in multimodal understanding.
On MMMU-Pro, which combines vision and text, Gemini 3.5 Flash scores 84%, the highest of all evaluated models. In science, it scores 92.2% on GPQA Diamond, while Gemini 3.1 Pro leads that benchmark at 94.1%. The frontier flagships pull ahead on agentic coding, where GPT-5.5 (84.3%) and Claude Opus 4.8 (84.6%) lead Terminal-Bench v2.1.
Here is a detailed table of Google Gemini benchmarks compared to other popular models:
GPQA Diamond (Science)
92.2%
94.1%
93.2%
92.0%
90.1%
MMMU-Pro (Multimodal)
84%
82%
81%
-
78%
Terminal-Bench v2.1 (Agentic coding)
78.7%
73.8%
84.3%
84.6%
39.7%
AA-LCR (Long context reasoning)
69.3%
72.7%
74.3%
67.7%
64.3%
GDPval-AA v2 (Elo, real-world work)
1350
967
1494
1604
1090
How has Google Bard evolved into Gemini? What key milestones mark this transformation? The timeline below shows the development and enhancement stages of this language model. Let’s take a closer look.
As you can see, Google expands its AI capabilities and reach with each update. Recent improvements have focused on faster default models and stronger multimodal understanding, along with deeper integration across Google’s ecosystem.
This next generation promises even more powerful AI agent capabilities and represents Google’s continued push to compete with GPT-5.5 and Claude Opus 4.8.
The Google Gemini statistics reveal a robust and versatile language model that excels in various capabilities. With Gemini 3.5 Flash now the default and a context window of up to one million tokens, Gemini is a strong competitor to ChatGPT and other leading models.
As seen, Google’s solution offers advanced capabilities and versatile performance across multiple domains. Gemini 3.5 Flash, for instance, tops the MMMU-Pro multimodal benchmark and runs at 183.9 tokens per second. With 750 million monthly active app users and 2 billion monthly users on AI Overviews, Gemini’s reach is among the widest in the AI field.
Google Gemini is poised to influence businesses significantly. Its advanced AI capabilities can support various operations, from customer service to data analysis. Thus, companies might be able to drive efficiency and innovation.
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Google Gemini statistics reveal there were 334.5 million unique web visitors in May 2026, while the Gemini mobile app reached 750 million monthly active users by February 2026.
ChatGPT, Gemini, and Anthropic Claude are examples of large language models (LLMs). They are designed to generate human-like text based on the input they receive. Thus, they are capable of tasks such as text generation, translation, summarization, etc.
Google rebranded Bard to reflect the significant advancements made to the AI model. In the Google Bard vs Gemini comparison, the latter has improved performance. Moreover, the new version has broader functionalities. This rebranding also aligns with Google’s strategy to continuously innovate and provide state-of-the-art AI solutions.
Google Gemini is trained on information available up to early 2025. It might not have information on events that occurred after that point. However, Gemini can access real-time information via Google Search integration, enabling it to retrieve current data and answer questions about recent events beyond its training cutoff.
