Google’s Gemini has undergone a rapid transformation since late 2025, moving from a conversational AI experiment into a system that is increasingly embedded across the company’s products and services. The pace of updates has been steady, but the cumulative effect is what stands out. Gemini today behaves less like a standalone chatbot and more like an AI layer that connects search, productivity tools, and mobile experiences.
This shift is already visible across Google’s ecosystem. Gemini is now capable of handling tasks directly inside its interface, from generating documents and presentations to supporting meetings and workflows, as seen in updates that brought deeper integration with Workspace tools.
The most recent update reflects that shift in a practical way. Gemini now supports more natural continued conversations, allowing it to retain context across multiple prompts without requiring users to restate information. This builds on earlier work around memory and cross-app awareness, particularly inside Google Workspace, where Gemini can surface past interactions and use them to inform ongoing tasks.
Google has also been refining how users interact with Gemini across devices, including improvements to voice-based experiences and interface design. Recent updates have made conversations feel more fluid and less transactional, particularly in smart home and mobile environments where interaction needs to be immediate and intuitive.
That continuity changes how the system is used. Instead of issuing isolated prompts, users can now develop ideas, refine outputs, and move through tasks in a way that feels persistent rather than fragmented.
This update sits on top of deeper changes that have been rolling out since the end of 2025. Google’s newer Gemini models have introduced stronger reasoning, broader multimodal capabilities, and tighter integration with Search and Android. Taken together, these shifts point to a clear direction. Gemini is not being developed as a chatbot that competes on answers alone. It is being positioned as a system that can assist across workflows, adapt to context, and operate across the environments where people already spend their time.
What’s new in Google Gemini (April 2026)
Over the past few months, Google has expanded Gemini in ways that go beyond incremental improvements, focusing on how the system can be used across real workflows. The updates span productivity, personalization, and on-device AI, giving a clearer sense of how Gemini is evolving in practice rather than just in capability.
One of the more visible changes is how Gemini is being shaped into a productivity tool. Google has introduced features that allow users to organize their work more effectively, including the ability to structure conversations into dedicated projects and access notebook-style workflows directly on the web without a subscription. This builds on a broader push to make Gemini useful for ongoing tasks rather than one-off queries, especially as users begin to manage longer interactions and more complex inputs.
At the same time, Google is introducing ways to make Gemini more adaptable to individual users. Features like reusable prompt “skills” in Chrome allow users to save and reapply specific instructions, while improvements in image generation are moving toward outputs that reflect personal preferences by drawing on a user’s photo library. These changes point to a system that is not just responsive, but increasingly shaped by how each person uses it.
Some of the most important updates are happening at the system level. Google is continuing to push Gemini onto devices through its Nano models, with newer iterations expected to bring more capable on-device AI to upcoming Android hardware. Alongside this, interface updates like the redesigned Gemini Live experience are making it easier to interact with the system in real time, particularly on mobile. These changes suggest a focus not just on what Gemini can do, but where and how it is used.
Google is also beginning to explore more sensitive use cases, including mental health support features, though these remain early and are positioned as assistive rather than standalone solutions. This reflects a broader pattern in how Gemini is being deployed, where new capabilities are being introduced cautiously in areas that require a higher degree of trust.
Taken together, these updates show a clear direction. Gemini is not just expanding in capability, but in how it fits into everyday use, from structured projects and reusable workflows to more personal outputs and deeper device integration.
What is Google Gemini?
Google Gemini is Google’s generative AI platform, built around a family of models designed to process and generate text, images, audio, video, and code within a single system.
At its core, Gemini is a multimodal AI model. That means it can take different types of input, such as written queries, images, or voice, and interpret them together rather than in isolation. This allows it to understand context more effectively and produce responses that are more relevant and structured, especially in complex scenarios.
Gemini also represents a shift in how Google approaches AI products. Earlier tools like Google Assistant were built around commands and predefined actions, while Gemini is designed to handle open-ended queries, generate content, and support tasks that unfold over multiple steps. It is not just responding to requests, but interpreting intent and building responses that can evolve through interaction.
The platform itself has absorbed several of Google’s earlier AI efforts.
Bard was rebranded as Gemini, while Duet AI became part of Gemini’s integration within Workspace. Today, Gemini exists both as the underlying model and the user-facing assistant, accessible through web browsers, mobile devices, and across Google’s ecosystem.
Recent model updates have further expanded its capabilities. Newer versions are better at structured reasoning, allowing Gemini to process more complex questions and provide answers that are more detailed and targeted. This is particularly noticeable in tasks that involve analysis, research, and multi-step problem solving, where the system is expected to go beyond basic responses.
How is Gemini different from Google Assistant?
Google Assistant and Google Gemini are built on fundamentally different approaches to AI, and that difference shapes how they are used.
Google Assistant was designed as a task-oriented voice assistant. It relies on predefined commands and integrations to perform actions such as setting reminders, controlling smart home devices, or answering simple queries. Its responses are typically tied to specific triggers, which makes it fast and reliable for routine tasks, but limited when it comes to handling open-ended or complex requests.
Gemini, by contrast, is built on large language models that can generate responses dynamically. Instead of relying on fixed commands, it can interpret intent, process different types of input, and respond in a more flexible way. This allows it to handle tasks that go beyond simple execution, including writing, summarizing information, analyzing inputs, and supporting multi-step workflows.
The difference becomes most apparent in how each system handles context. Assistant generally treats each request as a separate command, while Gemini can carry context across interactions, allowing users to refine queries and build on previous responses. This makes it more suitable for tasks that require iteration rather than immediate execution.
Google is now positioning Gemini as the next step beyond Assistant, particularly on Android devices, where it is gradually taking over as the primary AI interface. While Assistant still handles many system-level functions, Gemini is increasingly responsible for broader interaction, content generation, and more complex user queries.
What can Gemini do?
Gemini’s capabilities continue to expand, but they are still defined by the environments in which the system operates. While it can generate content, analyze information, and guide complex tasks, it does not directly execute actions in the physical world. Google is exploring that possibility through its robotics efforts, where Gemini-like systems are being tested in real-world environments that extend beyond screens and devices.
In day-to-day use, however, the effectiveness of Gemini often comes down to how it is used. Because it relies on user input to guide responses, the quality and clarity of prompts can significantly influence the results. Learning how to structure queries and refine instructions is an important part of getting consistent, useful output from the system.
Video creation
Gemini’s involvement in video creation is still developing, but it already plays a meaningful role in the planning and conceptual stages. It can take a rough idea and expand it into a structured script, suggest scene breakdowns, and help organize the flow of a narrative. It is also beginning to move into direct video generation, with newer tools capable of producing short-form visual outputs from prompts, as seen in early implementations within Gemini Advanced.
Where Gemini becomes more useful is in iteration. Video production rarely follows a linear path. Scripts are rewritten, sequences are reordered, and ideas evolve as the project develops. With continued conversation, Gemini can retain the context of earlier drafts and feedback, allowing users to refine their work without starting from scratch each time. This makes it more practical as a collaborative tool rather than a one-time generator.
Information processing
Processing information is still one of Gemini’s strongest capabilities, but the nature of its output has improved with newer models. The system is better at handling queries that require synthesis rather than simple summarization.
For example, when comparing multiple products, analyzing a topic from different perspectives, or breaking down a complex concept, Gemini can organize its response in a way that reflects the structure of the problem. This is a direct result of improvements in reasoning, which allow the system to handle multiple variables within a single response.
The difference is subtle but important. Earlier versions of AI systems often produced answers that were technically correct but lacked depth or structure. Gemini’s newer models are more capable of presenting information in a way that is easier to follow and apply.
Image creation
Image generation within Gemini has become more flexible and iterative. Instead of relying on a single prompt to produce a final output, users can refine images through conversation, adjusting elements such as style, composition, and tone.
This approach changes how image generation is used. Rather than treating it as a one-step process, it becomes part of a broader workflow where ideas can evolve gradually. For designers and content creators, this makes the tool more adaptable, particularly when working toward a specific visual outcome.
Research
Research is one of the areas where Gemini’s improvements in reasoning and context retention have the most impact. The system can handle tasks that involve gathering information, organizing it, and refining it over multiple steps, with newer capabilities allowing it to explore topics through structured, multi-step analysis, similar to how Google is positioning its deeper research tools within Gemini itself.
This makes it useful for projects that require depth rather than quick answers. Users can begin with a broad topic, narrow it down through follow-up queries, and shape the output into a structured format without losing continuity. The ability to maintain context across these steps is what allows Gemini to support workflows rather than just individual queries.
Gemini Live
Gemini Live introduces a more natural form of interaction through voice. Users can speak directly to the system, interrupt responses, and guide the conversation in real time, creating an experience that feels closer to a discussion than a sequence of commands, particularly as Google continues refining how conversational interactions behave across devices.
The importance of this feature lies in accessibility and convenience. On mobile devices, voice interaction often feels more immediate than typing, especially when dealing with quick questions or ongoing tasks. Early hands-on experiences with Gemini Live also highlight how these interactions are evolving beyond simple voice commands into something more fluid and responsive.
Multimodal capabilities and ecosystem integration
One of Gemini’s defining characteristics is its ability to work across multiple types of input. It can process text, images, audio, and video within a single interaction, allowing users to combine different forms of information without switching tools.
This capability becomes more meaningful when paired with its integration across Google’s ecosystem. In Search, Gemini enables follow-up queries and deeper exploration of topics. In Workspace, it assists with documents and emails while drawing on existing context. These integrations change how users move between tasks, allowing them to carry intent across different environments.
How Gemini is moving toward AI agents
A significant shift in Gemini’s development is its movement toward handling tasks that unfold over time. Instead of responding to individual prompts, the system is becoming more capable of supporting workflows that involve planning, execution, and refinement.
This is often described as a move toward agentic AI. In practical terms, it means the system can assist with tasks that require multiple steps, such as research projects, content creation, or problem-solving processes. The ability to retain context plays a central role here, as it allows Gemini to build on previous interactions rather than starting over.
Personal AI and context awareness
Personalization is becoming a central part of how Gemini operates. By integrating with Google’s services, the system can draw on documents, emails, and past interactions to provide responses that are more relevant to the user.
This shift has important implications. A system that understands context can offer suggestions that are more aligned with individual needs, reducing the amount of input required from the user. At the same time, it raises questions about how data is used and managed, making user controls and transparency increasingly important.
Generative interfaces and how results are changing
Gemini is also influencing how information is presented. Instead of returning static blocks of text, the system is beginning to generate structured outputs that can be interacted with and refined.
These outputs might include organized summaries, multi-part responses, or formats that can be edited directly. The goal is to make the information more usable, allowing users to engage with it rather than simply read it. This represents a shift from AI as a source of answers to AI as a tool for building and refining content.
Gemini inside Google Search
Gemini’s integration into Search changes how users interact with information online. Instead of receiving a list of links, users can explore topics through follow-up questions, refining their queries within the same session.
This creates a more interactive search experience, where information is not just retrieved but shaped through conversation. It also reflects a broader change in how search engines operate, with AI playing a more active role in guiding users through information.
Gemini beyond consumers
Google is extending Gemini into enterprise environments, where its ability to handle workflows and integrate with existing tools becomes more valuable. Businesses can use it to automate tasks, manage documents, and support internal processes.
This expansion reflects a broader strategy. Gemini is not just a consumer-facing product. It is being positioned as a platform that can operate across different scales, from individual users to large organizations.
Performance and infrastructure
The capabilities of Gemini are supported by Google’s underlying infrastructure, including custom hardware designed for AI workloads. These systems enable the processing of large and complex inputs while maintaining performance across different types of tasks.
Improvements in efficiency and scalability are critical to how Gemini operates. They allow the system to deliver faster responses, handle multimodal inputs, and support more advanced reasoning without compromising usability.
Which devices can use Gemini?
Gemini is available across a range of devices, including mobile phones, laptops, and desktop systems. On a PC or laptop, it can be accessed through any modern web browser via the Gemini website, offering a similar experience to other AI chat platforms. For those accessing the AI on your mobile phone, Google Gemini is currently available as an app for Android and on iOS devices.
In terms of hardware requirements, Gemini runs on most modern smartphones, though performance can vary depending on the device. Android phone users are able to switch Google Assistant over to Gemini in their settings. At the moment, the requirements for this include a phone which runs Android 10 or higher and also has more than 2GB of RAM in order to support the AI. The majority of modern devices will meet this criteria but those with older gadgets may find that Gemini isn’t available to them. Newer Android flagships, particularly those with on-device AI capabilities, are better equipped to handle more advanced features, while older devices may have limited access or reduced functionality.
Gemini even works with Samsung devices and will tap into native Samsung apps for information it might need.
How much does Gemini cost?
Basic Gemini features, such as the chatbot, can be used for free. This doesn’t integrate into your other Google products but can still do basic tasks like gathering information and drafting emails for you.
You can also subscribe to Google AI Pro which comes in at $20 per month. This allows you to use the full suite of AI tools from Google including video generation through the Veo 2 Engine. You can grab a free trial of Google AI Pro, which had me absolutely hooked after trying it out. Alongside the AI tools you can access, you also get more storage in your Google Drive.
If you want access to the very latest AI that Google has to offer – including Veo 3 – you’ll need to opt for Google AI Ultra plan, which is a whopping $249.99 per month.
How to change Gemini’s settings
How you change Gemini’s settings will depend on which device you access the AI from. For those who use the Gemini app, you can tap your profile picture (or initial if you don’t have a profile picture) and then select Settings.
For those using Gemini through the Google App, you can tap your profile picture or initial and then select Settings and then press the Gemini option for the more specific settings.
If you’ve opted to have Gemini replace Google Assistant on your Android phone then you can adjust your settings by opening your device’s settings, then search for Gemini or Google Assistant features and then select the app or related setting you want to adjust.
Which is better: Siri or Gemini?
Comparing Siri to Gemini is like comparing a 90’s mobile to a smartphone. One is far more advanced than the the other.
While Siri is technically an AI, it’s not based on a LLM meaning – similar to Google Assistant – it just spits out predetermined answers rather than actually processing and understanding information. This makes Gemini the far superior option.
At the moment there is hope for Siri to improve when Apple rolls out the new and improved version of its voice assistant with Apple Intelligence, the company is already far behind schedule on that front – so much so that I’m considering swapping from my iPhone to a Google Pixel.
Siri would need to play a lot of catch-up to get anywhere close to what Gemini is currently capable of, and Gemini’s head start makes the possibility of Siri closing the gap a slim one.
If you’re an iPhone user and debating whether to wait for Siri to get better or choose Gemini, just go ahead and download the iOS Gemini app. You’ll get far more use out of it in the meantime, and you can always make the jump back to Siri if the new version ever drops.
Gemini vs ChatGPT: Which one should you use?
Google Gemini and ChatGPT are often compared because they solve similar problems on the surface, but they are built with different priorities, and that difference shows up in everyday use.
Gemini’s biggest advantage comes from how deeply it is integrated into Google’s ecosystem. It works across Search, Gmail, Docs, and Android in a way that allows it to pull context from multiple sources and apply it within a single interaction. For users who already rely on Google’s services, this creates a more connected experience. Tasks like summarizing emails, working with documents, or refining search queries feel more seamless because the system is already embedded where the work is happening.
ChatGPT, by contrast, operates more independently. It is not tied to a single ecosystem in the same way, which gives it more flexibility across platforms and use cases. It tends to feel more consistent as a standalone tool, particularly for writing, coding, and general problem-solving tasks. Many users also find its responses more refined in tone and structure, especially for long-form content.
The difference becomes clearer when looking at how each system handles context. Gemini’s recent updates around continued conversation and memory make it better suited for workflows that evolve over time, particularly when tied to Google apps. ChatGPT, while also capable of maintaining context within a session, still feels more session-based unless users actively structure their interactions.
There is also a distinction in how each system approaches reasoning. Both have improved significantly, but they emphasize different strengths. Gemini is increasingly focused on integrating reasoning into real-world workflows, especially in areas like research and productivity, a shift that becomes clearer when comparing how both platforms approach features and capabilities in practice.
For multimodal use, both platforms now support images, voice, and other formats, but Gemini’s approach is more tightly woven into its ecosystem. This allows it to combine inputs across services, while ChatGPT’s multimodal features are more contained within its own interface.
In practical terms, the choice comes down to how you plan to use the tool. Gemini is better suited for users who want an AI system that works across Google’s products and supports ongoing tasks with context. ChatGPT remains a strong choice for users who prefer a standalone tool with consistent performance across a wide range of use cases, especially when evaluated across real-world testing scenarios.
Gemini vs Microsoft Copilot: The enterprise angle
Microsoft Copilot enters the conversation from a different angle, particularly for users working within Microsoft’s ecosystem.
Copilot is deeply integrated into tools like Word, Excel, and Teams, making it particularly effective in structured, enterprise-heavy workflows. It is designed to enhance productivity within those environments, with a strong focus on documents, spreadsheets, and collaboration.
Gemini’s approach is similar in intent but broader in scope. It spans consumer and enterprise use cases, integrating across Search, Android, and Workspace. This gives it an advantage in flexibility, particularly for users who move between personal and professional tasks within Google’s ecosystem.
The distinction here is less about capability and more about environment. Copilot feels most natural inside Microsoft’s suite of tools, while Gemini is designed to operate across Google’s platforms. Choosing between them often depends on which ecosystem a user is already invested in.
Which AI is actually better?
There is no single answer to this, because each system is optimized for a different kind of use.
Gemini stands out for its integration, context awareness, and ability to support workflows that span multiple tools. It is evolving toward a system that feels embedded in everyday tasks rather than separate from them.
ChatGPT remains one of the most consistent and versatile standalone AI tools, particularly for writing, coding, and general-purpose use.
Copilot is strongest in structured, enterprise environments, where integration with Microsoft tools is a priority.
Siri, while still useful for quick commands, operates on a different level and is not designed to compete with the broader capabilities of these systems.
The more relevant question is not which one is better overall, but which one fits the way you work. As these systems continue to evolve, the differences between them are becoming less about raw capability and more about how and where they are used.
