What is behind Google Gemini?
Google Gemini comprises a family of multimodal large language models capable of understanding and generating text, images, videos and programming code. This definition contains two terms that warrant further explanation to aid a better understanding of Google Gemini.
In the field of artificial intelligence, the term ‘large language models’ (LLMs for short) primarily refers to neural networks that are capable of understanding, processing and generating human language in various ways. The term ‘large’ describes the fact that these models are trained on vast amounts of data and possess several billion neurons or parameters that recognise the underlying structures in the text.
Multimodal models are a subfield of machine learning and encompass architectures capable of processing multiple types of data, known as modalities.
Google is now releasing new Gemini models at intervals of just a few weeks: three new Flash models were released between July and September 2026, followed by Gemini 4 Argon on 30 September. Google publishes all updates on the Gemini blog. For users and businesses, Gemini offers enhanced, multimodal AI support for text, images, speech, music, planning and scalable enterprise solutions. For developers, greater control and flexibility are provided through deep ‘thinking’, expanded media inputs, structured outputs and more tool integrations than ever before. This means faster implementation for software projects – such as prototypes – using the Gemini app, developer tools and the combination of Antigravity and Gemini 3. The key trend is the shift towards true agent-based AI, enabling the AI to independently carry out multi-step tasks.
The Most Important Features of Gemini
Gemini is now integrated into almost all Google products, from Google Search and Chrome to Android, Gmail and Docs. The models generate text, images, videos and music, and – with your permission – can access content from Google services to carry out tasks.
Here are a few possible uses of the Gemini models:
- Marketing and content creation: automated texts for posts, captions, campaign planning and image generation
- Productivity and workspace: Summaries of documents, slides, and transcripts with personal intelligence as a connection between Google products
- Research and analysis: Report generation, multimodal analysis and step-by-step task processing
- Coding capabilities: Code generation, debugging and refactoring, and automated application development
Interpretation and Generation With Native Multimodality
Like OpenAI’s GPT models, Gemini is also multimodal: it processes text, images, audio, video and PDFs. Google has trained Gemini to be multimodal from the outset – that is, natively. This enables the model to combine information from different formats in a single response, such as a diagram from a PDF with a spoken commentary. Gemini can, for example, programme and deploy a complete application simply by analysing an image. This makes it possible to recreate websites, for instance, by providing Gemini with a screenshot of the current page. Whilst a screenshot cannot capture the full complexity of a website or a programme, it serves as a good starting point for further programming.
Image Generation and Video Creation
For images, Google relies on the Nano Banana models. In early October 2026, Google released Nano Banana 2.1, which, according to Google, offers better image quality, more accurate interpretation of prompts, more consistent figures across multiple editing steps, and improved rendering of text within images. You can combine text and images – for example, to create a new image from two photos.
Gemini generates videos using Gemini Omni Flash, which Google unveiled at I/O in May 2026. Omni Flash takes text, images, audio and videos as input and uses them to create video clips that you can further edit via conversation. The model has been generally available in the Gemini API since August 2026, with resolutions of up to 4K (upscaled). All videos generated with Omni bear Google’s invisible SynthID watermark.
Agentic Capabilities
By 2026, Gemini will be clearly positioned as an agent-based system that carries out tasks independently.
- Deep Research: A research assistant that independently searches through hundreds of sources – including Gmail and Drive, if requested – and compiles a report with references.
- Gemini Spark: Google’s 24/7 assistant that continues to work in the cloud even when your device is switched off. Spark is not yet available in Germany or the rest of the EEA.
- Skills (replacing Gems): With Skills, you specify how Gemini should carry out a particular task. Unlike the previous Gems, multiple Skills can be combined in a single query. From 17 November 2026, Google will automatically convert existing Gems to Skills.
- Live API: For real-time voice applications, Google released Gemini 3.8 Live on 15 September 2026, an audio-to-audio model for voice agents.
Which Versions of Gemini Are There?
Google unveiled Gemini on 6 December 2023, initially in the Ultra, Pro and Nano variants. Since then, Google has expanded Gemini’s context windows, reasoning and agent-like capabilities with each successive generation – Gemini 1.5, 2.0, 2.5 and 3.
Gemini 4 Argon
At the end of September 2026, Google announced Gemini 4 Argon, the first model in the fourth generation. According to Google, Argon is designed for complex, time-consuming tasks in software development, knowledge-based work such as law and finance, and cyber defence.
- Output limit: up to 1 million tokens per response
- Benchmarks according to Google: new record on DeepSWE v1.1 for time-consuming software tasks
- Security: according to Google, the most robust Gemini model to date against indirect prompt injections
- Availability: initially only for verified organisations in the Fairwind Programm
- API pricing: US$2 per million input tokens and US$10 per million output tokens at launch, rising to US$4 and US$20 thereafter
Gemini 3
The Gemini 3 series was launched on 18 November 2025 with Gemini 3 Pro. Since then, Google has been continuously expanding the range: Gemini 3.1 Pro (February 2026) remains the most powerful model in the Gemini app to this day. Four ‘Flash’ generations followed in the space of four months: Gemini 3.5 Flash (May), 3.6 Flash (July), 3.7 Flash (August) and 3.8 Flash (September). Free users of the Gemini app are currently using Gemini 3.6 Flash.
- End users can access the models via the Gemini app and through web and browser integrations (including Search and Chrome)
- Developers can use them via the Gemini API, Google AI Studio, Vertex AI and the agent-oriented development environment Google Antigravity
- Businesses and enterprise customers can access them via Gemini Enterprise, subject to admin approvals in the control panel
Alongside the release of Gemini 3, the new development platform Google Antigravity – an agent-first IDE – was unveiled at the end of 2025.
Gemini-3 models process inputs with a context window of around one million tokens. Developers use the `media_resolution` parameter to control the resolution at which images and videos are processed. With `thinking_level`, they determine how thoroughly the model thinks before responding. Another advance is the generation of generative user interfaces (UI); developers can generate interactive web UIs directly from prompts. Google has presented the following example:
Discontinuation of models at Gemini
The following models have already been discontinued, Google lists all dates on the Gemini deprecations page.
How Can Google Gemini be used?
Gemini models are available in three ways:
- For end users: in the Gemini app (Web, Android, iOS, macOS, Windows), in Google Search, and in Google services such as Gmail and Docs. Free accounts can use Gemini models with higher limits at no cost. On Android smartphones, Gemini replaces Google Assistant.
- For developers via the Gemini API in Google AI Studio, the Gemini CLI, Android Studio and the agent-based development platform Google Antigravity.
- For businesses via Gemini Enterprise and the Gemini Enterprise Agent Platform (formerly Vertex AI), with admin controls and data protection commitments.
You can find pricing, subscriptions and tips on data protection in the article ‘What is Google Gemini (formerly Bard)?’.
Google Gemini or GPT?
Google’s Gemini and OpenAI’s GPT will be close competitors in 2026, albeit with complementary strengths.
When OpenAI launched ChatGPT in November 2022, Google followed suit in March 2023 with Bard, the predecessor to Gemini. According to Google, over 900 million people now use the Gemini app every month.
By the end of 2026, two generations of models in particular will be competing: OpenAI released GPT-6 Astra in September, whilst Google announced Gemini 4 Argon. Google states that Argon outperforms GPT-6 Astra in several benchmarks. Independent testing has so far been virtually impossible, as Argon is not yet widely available. In everyday use, most users work with Gemini 3.x or the GPT models in ChatGPT anyway. Which model is the better choice depends on the use case and changes with every update. Those who work extensively with Google services benefit from Gemini, whilst those who wish to remain independent of any particular ecosystem are better off with ChatGPT.
However, alongside these market leaders, we should not overlook the other competing chatbot systems and large language models, which, for example, also stand out because they require less computing power. To this end, we offer a detailed overview of 20 ChatGPT alternatives.
Conclusion
Google Gemini has become one of the leading families of AI models. With Gemini 4 Argon, Google is challenging the top of the market, whilst its Flash models deliver faster and more affordable everyday models on a weekly basis. Its strengths lie in its native multimodality, media generation with Nano Banana and Gemini Omni, and deep integration with Google services. Whether Gemini or GPT is the better fit depends on the specific use case.
One thing is clear: both systems will set the standard in the AI landscape in 2026 and drive competition forward.
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