Gemini 4: 7 Powerful Things Google’s New AI Model Can Do

Last Updated: 1 October 2026

Gemini 4 is Google’s brand-new flagship AI model, and it could change the way we code, research and stay safe online. Launched on 30 September 2026 under the name Gemini 4 Argon, it is the most powerful artificial intelligence system Google has ever built. The company claims it beats rival models from OpenAI and Anthropic across a range of benchmarks, and it arrives with a surprising speciality: cybersecurity defence.

For readers in the UK, the timing could not be more interesting. British businesses lose billions of pounds to cybercrime every year, developers are under constant pressure to ship software faster, and artificial intelligence tools are now part of everyday office life. This guide breaks down exactly what it is, the seven most powerful things the model can do, how it compares with GPT-6 Astra and Anthropic’s Fable, and how you can try it yourself.

What is Gemini 4? Gemini 4 is Google’s latest flagship AI model, launched on 30 September 2026 under the codename Argon. It is built for deep reasoning across coding, research, writing and cybersecurity, and can autonomously find and patch critical software flaws. It currently tops leading industry benchmarks and is rolling out first to cyber defenders, then to developers, businesses and everyday users.

What Is Gemini 4 Argon? A Simple Definition

Let us start with the basics. Gemini 4 Argon is a frontier artificial intelligence model — Google’s term for its most advanced class of AI system — developed by Google DeepMind and launched by Alphabet on 30 September 2026. It follows on from the Gemini family that powers the Gemini app, which Google says passed one billion monthly users in August 2026.

The “Argon” name marks a fresh start. According to reports, Google cancelled its planned Gemini 3.5 Pro and shifted focus away from lighter models such as Gemini 3.8 Flash, choosing instead to go all-in on large-scale frontier intelligence. In plain language, that means a bigger, more capable model designed to handle harder problems.

Google describes Argon as “built to sustain deep reasoning across complex, long-horizon workflows”. That is a mouthful, so here is what it means in practice:

• Deep reasoning: the model can think through multi-step problems rather than just answering simple questions.
• Long-horizon workflows: it can stick with a task for a very long time, working through thousands of steps if needed.
• Complex tasks: it is aimed at real professional work such as software engineering, legal research and financial analysis, not only chat.

With that foundation in place, let us look at the seven most powerful things this new model can actually do.

1. It Can Find and Fix Security Flaws on Its Own

The headline feature of Gemini 4 is cybersecurity. Google says the model was trained specifically for defensive cyber work and can “autonomously find, validate, and patch critical software vulnerabilities”, as reported in TechCrunch’s report on the Gemini 4 launch.

Think about what that means. A software vulnerability is a weakness in a program that criminals can exploit to break in. Traditionally, finding these flaws takes teams of security experts many hours of painstaking work. It can scan through code, spot the weakness, check that it is real and then write the fix — largely on its own.

The numbers back up the claim. On CWE-bench v1, a benchmark that tests how well a model can remediate security vulnerabilities, Argon ties for first place with a score of 68%. For UK businesses, this matters enormously. Cyberattacks cost the British economy billions each year, and the National Cyber Security Centre has repeatedly warned that small and medium firms are the most exposed.

There is a catch, though. Because the same skills could be misused, Google is being careful. Argon is rolling out first only to a select group of trusted cyber partners through Google’s Fairwind Program, and Google says it is working with the US government on pre-release safety checks. For trusted defenders, the model will even be available without its usual cyber guardrails so they can use its full defensive power.

2. It Supercharges Coding and Engineering

Alongside security, coding is where Gemini 4 Argon is expected to shine brightest. Google says its own engineers are already using the model every day for real work, including debugging and large codebase migrations. That is a strong vote of confidence: when the people who built a tool rely on it for their jobs, it usually means something.

A codebase migration is one of the most painful jobs in software development. It means moving an old program — sometimes millions of lines of code — onto newer technology without breaking it. It is slow, boring and error-prone for humans. An AI that can do this reliably would save engineering teams weeks of effort.

The benchmark results are striking. On DeepSWE v1.1, a respected test of real-world software engineering ability, Argon scored 77.9%. That puts it ahead of Claude Opus 5.5 at 74.2% and OpenAI’s GPT-6 Astra at 74.1%. For context, these are the flagship models of Google’s two biggest rivals, and Argon beat them both by a clear margin.

For the UK’s growing tech sector — from fintech startups in London to software houses in Manchester and Edinburgh — faster, more reliable AI coding help could mean lower costs and quicker product launches. Junior developers may use it as a tireless mentor, while senior engineers could hand it the tedious work and focus on design.

3. It Thinks Through Long, Complex Workflows

One of the most impressive technical leaps in Gemini 4 is how long it can think. The model’s output token limit has jumped to one million tokens, up from just 64,000 in earlier models. Tokens are the building blocks of AI text, so this is roughly a fifteen-fold increase in how much the model can produce in a single run.

Why does that matter? Imagine asking an AI to analyse an entire company’s financial records, or to work through a thousand-page legal contract clause by clause. Older models would run out of steam partway through. With a million-token output ceiling, it can tackle what Google calls “long-horizon” tasks in one go.

Google says this extra headroom “adds a new level of depth in reasoning to solve tough problems in one go”. Early signs suggest the model is already strong at enterprise knowledge work. It posted leading results on Vals Finance Agent v2, which tests multi-step financial research, and on Harvey’s Legal Agent Benchmark, which covers legal research and drafting. It also ranked first on AutomationBench with 51.3%, a test of end-to-end execution across core business functions.

For professionals in the City of London or legal chambers across the UK, this hints at a future where AI assistants handle the heavy lifting of research while humans focus on judgement and client relationships.

4. It Understands Video, Charts and Documents

Gemini 4 is not limited to text. Google highlights the model’s ability to parse visuals, whether that means analysing the contents of long videos or reading charts and diagrams. This multimodal skill — handling text, images and video together — is becoming the standard for top-tier AI models.

The standout figure here is a 91.7% score on LVBench, a benchmark that measures long video understanding, where Argon is described as state of the art. In practice, that could mean feeding the model a two-hour recorded lecture and asking for a summary, or uploading a complex business chart and asking what the numbers really show.

Consider some everyday examples of how this could help:

• Students could upload lecture recordings and get clear revision notes in minutes.
• Analysts could drop in a spreadsheet chart and ask for the key trends, explained in plain English.
• Content creators could get a long video transcribed, summarised and broken into chapters automatically.
• Researchers could search through hours of footage for one specific moment.

As video becomes the dominant format on the internet, an AI that truly understands moving pictures — not just still images — is a significant step forward.

5. It Beats the Competition on Benchmarks

The AI industry is fiercely competitive, and every lab claims its model is the best. So how does Gemini 4 actually stack up? According to Google, very well indeed. The company claims Argon scored significantly higher than OpenAI’s GPT-6 Astra and Anthropic’s Fable and Opus models across a variety of AI benchmarks.

Google points to Vals, an increasingly popular AI benchmarking startup, whose AI model index currently lists Argon as the leading model. That is an independent check, which carries more weight than a company’s own marketing. Here is how the three flagship rivals compare:

Model	Maker	Standout strength	Key benchmark result	Availability
Gemini 4 Argon	Google	Cybersecurity defence, long-horizon reasoning	DeepSWE v1.1: 77.9%; LVBench: 91.7%	Phased rollout via Fairwind Program, then AI Ultra and API
GPT-6 Astra	OpenAI	General-purpose flagship, huge user base	DeepSWE v1.1: 74.1%	Available to ChatGPT users (1bn monthly users)
Fable / Opus	Anthropic	Released earlier in 2026 to strong reviews	Claude Opus 5.5 DeepSWE v1.1: 74.2%	Available via Anthropic’s products and API

A word of caution is sensible here. Benchmarks are useful, but they are designed by humans and every company chooses the tests that flatter its model. As TechCrunch’s report on the launch notes, the top labs are rushing to outdo each other even as they warn that AI could spin out of control. Independent reporting also points out that Google has not announced a general public release date, so the gap between launch-day claims and real-world performance is still untested.

Even so, the breadth of Argon’s reported wins — coding, security, finance, legal work and video understanding — suggests this is more than marketing gloss. Google was once considered behind in the AI race; with the Gemini app now past a billion monthly users, it is firmly back in the fight.

6. Gemini 4 Pricing and How to Try It in the UK

One of the most practical questions is also the simplest: how much does it cost, and when can you use it? Google has confirmed an introductory price of $2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off the input price. In Google’s official announcement, the company says access will expand to developers, enterprises and consumers as soon as possible.

For UK readers, a few things are worth knowing. The dollar pricing has not yet been converted into official UK pound pricing, and there is no confirmed date for general consumer access in Britain. The rollout begins with trusted cyber defenders, then moves to Google AI Ultra subscribers and paid API customers. Here is what you can do right now:

1. Check your Google AI plan. If you subscribe to Google AI Ultra, you will be among the first consumers offered access. Review your subscription in your Google account settings.
2. Join the API waitlist if you are a developer. Paid API customers are next in line after the cyber partners. Register your interest through Google’s developer channels so you are notified when Argon opens up.
3. Watch the Gemini app. With over a billion monthly users, the Gemini app is the most likely place for its features to appear for everyday users. Keep the app updated.
4. Follow Google’s safety updates. Because of the phased safety rollout, wider access depends on feedback from early testers. Official announcements will confirm when UK consumers can use it.
5. Budget for tokens if you run a business. At the introductory rates, heavy use could add up. Estimate your expected input and output volumes before committing.

Patience is required, but the direction is clear: Google wants the model in the hands of as many people as possible, and the UK will be a key market given the size of its tech and financial sectors.

7. What Gemini 4 Means for Everyday Users

Not everyone is a software engineer or a security analyst, so what does it mean for ordinary people? Quite a lot, as it happens. The same deep-reasoning engine that patches vulnerabilities can also help with the writing, research and planning tasks millions of us do every day.

Google built Argon for coding, research and writing as well as security. That means sharper help with drafting emails and reports, deeper research that pulls together long documents, and creative writing assistance that can sustain a plot or argument over many pages. The million-token output limit means it will not lose the thread halfway through a long project.

There is a bigger picture too. The Gemini app’s leap past one billion monthly users shows that AI assistants have gone mainstream, matching ChatGPT’s scale. As models like this get smarter, the gap between what experts can do and what anyone can do keeps shrinking. A small business owner in Birmingham will soon have research and coding help that once required a team of specialists.

Of course, smarter AI also raises real questions about safety, jobs and misinformation — questions Google itself acknowledges by rolling it out in careful phases. The technology is moving fast, and keeping up with it is becoming a skill in itself.

Final Thoughts: Should You Be Excited About Gemini 4?

Gemini 4 Argon is Google’s boldest AI launch to date, and the early evidence suggests the excitement is justified. A model that can autonomously patch security flaws, out-code its rivals, reason across million-token workflows and understand long videos is genuinely a step forward — not just an incremental upgrade.

For UK readers, the practical takeaway is simple: keep an eye on the Gemini app and Google’s announcements, because Gemini 4 features will reach consumers sooner than you might think. Whether you are a developer, a business owner or simply curious about AI, this is a launch worth following.

Want to stay ahead of the AI curve? Bookmark this page and check back for updates as the rollout widens. Share this guide with anyone who works in tech, and tell us in the comments: what would you ask Google’s most powerful AI model to do first?