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.
This article dives into the detailed Google Gemini statistics. We will 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
Language Model
LaMDA (Language Models for Dialog Applications)
Model Variants
Gemini 1.0 Pro: For natural language tasks and code generation
Gemini 1.5 Pro: For complex reasoning tasks, e.g., code and text generation, text editing, problem-solving, etc.
Gemini 1.5 Flash: For fast and versatile performance across a diverse variety of tasks
Gemini 1.0 Ultra: For tackling highly complex tasks demanding a vast understanding of the world and intricate reasoning abilities
Gemini 1.0 Nano: For tasks that need to happen directly on a device, e.g., basic text processing or speech recognition
Training Dataset Size
Gemini Pro: Around 5.5 trillion tokens
Gemini Ultra: Estimated to be roughly double that of Pro. Thus, somewhere around 11 trillion tokens
Knowledge Cutoff
Early 2023; updates after this period may not be included
Websites Like Gemini
ChatGPT, Microsoft Bing AI, Claude
Source: Google AI for Developers, Google Blog
Now that we got to know what Google Gemini is, let’s dive into the usage stats.
Google Gemini attracts millions of users globally. Let’s review its performance in several key usage trends.
There were 143.1 million desktop visits and 170.8 million mobile visits in May 2024.
In February 2024, Google Gemini statistics showed 134.4 million visits on desktop and 181.7 million on mobile. Visits peaked in March with 182.6 million on desktop (a 35.84% increase) and 250.8 million on mobile (a 38.07% increase). However, traffic declined in the subsequent months. Specifically, there were 143.1 million desktop visits and 170.8 million mobile visits in May 2024.
In the Claude vs Google Gemini vs ChatGPT comparison, the latter dominates in total visits. From February to April 2024, ChatGPT had an impressive 5.202 billion total visits. It significantly outpaced Google Gemini’s 1.164 billion. Meanwhile, Claude recorded 141.2 million.
Google Gemini attracted 60.05 million unique visitors in April 2024.
In February 2024, the platform had 65.64 million unique visitors overall. There was a slight decline, with unique visitors dropping to 60.05 million by April. Gemini number of users on desktop started at 25.29 million and decreased slightly to 24.41 million. Meanwhile, mobile unique visitors dropped from 40.34 million to 35.64 million over the same period.
The bounce rate on mobile increased to 34.27% in May 2024.
Google Gemini statistics by month show a relatively stable bounce rate with slight fluctuations. In February 2024, the desktop bounce rate was 26.50%. The value increased slightly to 29.35% by May. The mobile indicator saw a minor increase from 32.25% in February to 34.27% in May.
February 24
26.50%
–
32.25%
–
March 24
29.09%
+9.77%
32.49%
+0.74%
April 24
29.44%
+1.20%
33.81%
+4.06%
May 24
29.35%
-0.31%
34.27%
+1.36%
There is an average of 5.11 pages per visit on mobile in May 2024.
Engagement on Google Gemini is evident through the pages per visit metric. Desktop users visited an average of 3.3 pages per session in February, maintaining this level in May. Mobile users averaged 5.01 pages per visit in February, ending at 5.11 pages per visit in May.
February 24
3.3
–
5.01
–
March 24
3.19
-3.28%
5.22
+4.13%
April 24
3.22
+0.80%
4.94
-5.33%
May 24
3.3
+2.36%
5.11
+3.40%
The average visit duration on mobile was 8 minutes and 1 second in May 2024.
The average visit duration for desktop users was 4 minutes and 16 seconds in February. It slightly increased to 4 minutes and 22 seconds by May. Mobile users had a more extended visit duration. Specifically, it started at 7 minutes and 55 seconds. There were minor fluctuations in May, ending at 8 minutes and 1 second.
February 24
0:04:16
–
0:07:55
–
March 24
0:04:13
-0.91%
0:08:29
+7.22%
April 24
0:04:14
+0.14%
0:08:01
-5.43%
May 24
0:04:22
+3.35%
0:08:01
–
Google Gemini is a leader in user engagement compared to ChatGPT and Claude.
According to the latest Google Gemini statistics, the platform records 4.28 pages per visit on average. In contrast, ChatGPT has 3.94 pages per visit, while Claude trails with 3.45 pages per visit. Thus, we can conclude that Google’s model has higher user engagement and interaction per session.
However, ChatGPT leads in other metrics, such as monthly visits, with 1.734 billion compared to Google Gemini’s 388.0 million and Claude’s 47.07 million.
Monthly visits
388.0M
1.734B
47.07M
Monthly unique visitors
62.60M
208.8M
7.855M
Visits / Unique visitors
6.2
8.31
5.99
Visit duration
0:06:30
0:07:52
0:05:34
Pages per visit
4.28
3.94
3.45
Bounce rate
31.03%
31.42%
39.21%
Page views
1.662B
6.836B
162.4M
Google Gemini leads in mobile usage compared to ChatGPT and Claude.
Google Gemini statistics show the model’s strong presence in mobile app usage. Specifically, 57.40% of its user base is on mobile devices. In contrast, ChatGPT has a higher desktop usage at 62.40%, while only 37.60% of its users are on mobile. Similarly, Claude has 73.80% of its users on desktop and only 26.20% on mobile.
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 April 2024.
As of April 2024, the United States led the traffic sources for Google Gemini with 19.66%. India followed with 9.18%, Brazil – with 4.38%, the United Kingdom – 3.36%, and Colombia – 3.32%. The rest of the world contributed 60.1% of the traffic.
The largest age group of Google Gemini users is 25-34. It makes up 33.38% of the audience.
Google Gemini statistics highlight that the 25-34 age group is the most significant demographic. It comprises 33.38% of the audience. The 18-24 age group follows with 25.5%, while the 35-44 group accounts for 19.04%. Smaller segments include the 45-54 group at 11.95%, the 55-64 group at 6.34%, and users aged 65 and above at 3.79%.
Male users represent 58.34% of Google Gemini’s audience.
Gender distribution data indicates that male users dominate the platform. This segment made up 58.34% of the audience. Meanwhile, the female counterparts constitute 41.66%.
Direct traffic to Google Gemini peaked at 292.2 million in March 2024.
Direct traffic peaked at 292.2 million in March 2024, up from 209 million in February. Although there was a slight decline to 283.7 million in April, the Google Gemini statistics showed a 35.74% increase from February. Social media traffic also experienced a rise, with a 36.87% increase from February to April. Organic search traffic grew by 26.79%, while email traffic showed a modest increase of 1.46%. Conversely, referral traffic decreased by 15.61% over the same period.
Direct
209M
292.2M
283.7M
+35.74%
93.81K
163.98K
128.16K
+1.46%
Referrals
11.4M
10.84M
9.62M
-15.61%
Social
5.94M
9.25M
8.13M
+36.87%
Organic Search
81.79M
111.0M
103.7M
+26.79%
Paid Search
7.80M
9.75M
8.95M
+14.74%
Display Ads
22.96K
43.44K
32.20K
+40.24%
Source: SimilarWeb
Let’s explore Google Gemini’s cost structure and compare it with other language models.
Gemini 1.5 Flash offers the most cost-effective performance among advanced AI models, with $0.9 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 – from the API.
Google Gemini statistics indicate that the Gemini 1.5 Flash value is $0.9 per million input or output tokens. It is a cost-effective option, especially when compared to other models.
For instance, Gemini 1.0 Pro is priced at $1.5 per million input or output tokens. On the higher end, GPT-4 costs $7.5 per million input tokens and $15 per million output ones. Claude 3 Opus is the most expensive, with $15 for input and $75 for output tokens.
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 1.5 Pro boasts a context window of up to two million tokens, the longest of any large-scale foundation model.
Gemini 1.5 Pro and 1.5 Flash both have a default context window of up to one million tokens. Thus, these models enable near-perfect recall on long-context retrieval tasks across many formats. The latter include long documents, lines of code, audio, video, and more. For 1.5 Pro, developers and enterprise customers can sign up to try a two-million-token context window.
Comparing the quality index, GPT-4 scores the highest at 100, while Gemini 1.5 Pro scores 88.
Artificial Analysis has administered the evaluation where a higher quality index indicated better language model performance. Let’s review the data in more detail.
GPT-4.0
100
GPT-4.0 Turbo
94
Claude 3 Opus
94
Gemini 1.5 Pro
88
Llama 3 (70B)
88
Gemini 1.5 Flash
76
Claude 3 Haiku
72
GPT-3.5 Turbo
65
Llama 3 (8B)
65
As a result, we can see the comparison of Google Gemini vs GPT-4. The latter leads with a quality score of 100. Meanwhile, Gemini 1.5 Pro scores 88, showcasing robust performance.
Gemini 1.5 Flash is the fastest among its competitors, processing 141 tokens per second.
Google Gemini statistics show that Gemini 1.5 Flash outperforms other models speed-wise. Specifically, it processes 141 tokens per second. Llama 3 (8B) follows with 121 tokens per second, and Claude 3 Haiku processes 101 tokens per second.
Gemini 1.5 Flash
141
Llama Flash
121
Claude 3 Haiku
101
GPT-4.0
74
Gemini 1.5 Pro
55
GPT-3.5 Turbo
52
Llama 3 (70B)
41
Claude 3 Opus
29
GPT-4 Turbo
23
GPT-4 processes 74 tokens per second, while Gemini 1.5 Pro – 55 tokens per second. GPT-3.5 Turbo handles 54 tokens per second.
Among all Gemini models, the 1.5 Pro version has the best performance. The second-best model is Gemini 1.0 Ultra.
Google Gemini statistics prove the 1.5 Pro version is the top-performing model. Google DeepMind carried out research on:
The general performance, measured by MMLU, showed Gemini 1.5 Pro achieving the highest score of 85.9%. Gemini 1.0 Pro scored the lowest at 71.8%. In reasoning tasks, as assessed by Big-Bench Hard, Gemini 1.5 Pro again led with an impressive 89.2%. Meanwhile, Gemini 1.0 Pro had the lowest score at 75.0%. For multilingual capabilities, evaluated by WMT23, Gemini 1.5 Pro topped the list at 75.3%. Again, Gemini 1.0 Pro was at the lower end with 71.7%.
Audio recognition, evaluated by FLEURS, was one of the weaker capabilities across all models. Gemini 1.5 Flash had the highest score of 9.8%, while Gemini 1.0 Pro had the lowest at 6.4%. Reasoning abilities, measured by GQPA, showed the best performance with Gemini 1.5 Pro at 46.2%. At the same time, Gemini 1.0 Pro scored the lowest at 27.9%. For mathematical problem-solving, Gemini 1.5 Pro led with 67.7%, and Gemini 1.0 Pro lagged with a score of 32.6%.
Source: Google DeepMind, Artificial Analysis
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 we can see, Google expands its AI capabilities and reach with each update.
The Google Gemini statistics reveal a robust and versatile language model that excels in various capabilities. With its comprehensive training data and advanced context window, Gemini stands as a competitor to ChatGPT and other leading models.
As seen, Google’s solution offers advanced capabilities and versatile performance across multiple domains. Gemini 1.5 Pro, for instance, excels in general knowledge, code generation, and mathematical reasoning. Additionally, its context window of up to two million tokens sets it apart from its counterparts.
Google Gemini is poised to influence businesses significantly. Its advanced AI capabilities can enhance various operations, from customer service to data analysis. Thus, companies might be able to drive efficiency and innovation.
Are you looking to develop a custom language model? Don’t hesitate to contact DOIT Staffing. Our team of experts is ready to help you harness the power of the latest AI technologies. Enhance your operations and engage your customers more effectively today!
Google Gemini statistics reveal there were 60.05 million unique visitors in April 2024 alone.
ChatGPT, Google Bard, 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 up-to-date with information and knowledge available up to early 2023. It might not have information on events that have occurred after that point.
