Gemini 4 Carbon: Benchmarks, Price and Who Gets It First

Gemini 4 Carbon is Google's newest frontier model, and it now sits at the top of the main independent leaderboard. Google announced it on October 28, 2026. Carbon scores 59 on the Artificial Analysis Intelligence Index v5.0, one point ahead of Claude Opus 5.5.

The twist is that almost nobody can use it yet. Google is giving it first to cyber defenders, because its biggest gains are in finding and fixing software flaws. That's unusual. Here's where Carbon leads, where it trails, what it will cost and how your team can get ready before access opens.

Disclosure: Van Data Team builds AI systems with models from several providers, including Google, Anthropic and OpenAI. This article was researched with Claude, and Anthropic's Claude Opus 5.5 is one of the models compared here. Benchmark scores are reported as Google's or Artificial Analysis's. We haven't tested Carbon ourselves, since it isn't open to us yet.

Key Takeaways

• Google announced Gemini 4 Carbon on October 28, 2026, two weeks after Gemini 4 Argon became widely available.
• It ranks first on the Artificial Analysis Intelligence Index v5.0 with 59 points. Claude Opus 5.5 has 58, GPT-6 Astra 57 and Argon 50.
• Its headline strength is cyber defense: 88.2% on SEC-bench Pro, 54.8% on ExploitGym and 71.0% on CWE-bench, all by Google's count.
• It trails Opus 5.5 on OSWorld-2.1 and GPT-6 Astra on FrontierSWE 2. Opus 5.5 also leads the Artificial Analysis Coding Agent Index.
• Access starts with a small group of cyber defenders. There's no date for the Gemini app or the API.
• Pricing starts at $2 and $10 per million input and output tokens, then doubles to $4 and $20.

What Is Gemini 4 Carbon?

Gemini 4 Carbon is a large language model from Google DeepMind. It's the second model in the Gemini 4 family, after Gemini 4 Argon. Google calls it its next frontier model, which means its most capable one.

Carbon is built on Argon, not trained as a separate line. Think upgrade, not reboot.

Google aimed it at three things: long, hard tasks, coding and cyber defense. It says Carbon can stay on one task for hours. The Decoder notes that its ExploitGym score came from runs of up to ten hours. Big claims.

Google also says Carbon is safer in day-to-day use. In Google's own tests, it makes about half as many unsupported claims as Argon. It also cut successful prompt injection attacks on the Gray Swan benchmark from 0.7% to zero.

Gemini 4 Carbon at a Glance

Item	Detail
Announced	October 28, 2026
Maker	Google DeepMind
Built on	Gemini 4 Argon
Independent rank	First on the Artificial Analysis Intelligence Index v5.0, with 59 points
Main strength	Cyber defense: finding, testing and patching software flaws
Access now	A small group of cyber defenders; more partners later through the Fairwind Program
Gemini app and API	No date given
Price per million tokens	$2 input and $10 output at first, then $4 and $20; cached input 95% off

How Big Is the Lead on the Index?

It's real, but it's small. Carbon has 59 points on the Artificial Analysis Intelligence Index v5.0. Claude Opus 5.5 has 58, GPT-6 Astra has 57 and Argon has 50.

Model	AA Intelligence Index v5.0	AA Coding Agent Index
Gemini 4 Carbon	59	59
Claude Opus 5.5	58	62
GPT-6 Astra	57	Not reported in launch coverage
Gemini 4 Argon	50	50

One point is a thin margin. A new release from any rival could flip the order in a week, and labs now ship new models every few weeks, so a one-point lead may not last long. What stands out more is the jump from Argon: nine points in about a month is a big gain for one model family.

Keep one detail in mind. Artificial Analysis changes its index from time to time, and the scores only compare within one version. On the older v4 index, Argon scored 53, not 50. So don't compare Carbon's 59 with numbers from earlier articles.

Artificial Analysis also tracks individual tests. By Trending Topics' count, Carbon has the top score on four of the index's nine, including AA-Omniscience and Terminal-Bench-Science. It doesn't win everywhere, and that matters for picking a model.

Where Gemini 4 Carbon Leads

Google's launch table compares Carbon with Argon, Claude Opus 5.5 and GPT-6 Astra across a long list of tests. Carbon has the top score on all but two of them. Google ran the cyber tests "without guardrails," so they show the raw model, not the safety-filtered product.

Here are the rows with the clearest gaps. These are Google's figures, as transcribed from its launch chart by CellCog, so treat them as vendor results until others repeat them.

Benchmark (what it tests)	Gemini 4 Carbon	Best rival	Gap (our math)
SEC-bench Pro (finding flaws)	88.2%	GPT-6 Astra, 85.4%	+2.8 points
ExploitGym (testing whether a flaw is real)	54.8%	GPT-6 Astra, 42.4%	+12.4 points
CWE-bench (patching flaws)	71.0%	Argon and Astra, 68.0%	+3.0 points
Terminal-Bench 4.0 (command-line tasks)	65.2%	Claude Opus 5.5, 59.6%	+5.6 points
GraphWalks, 256k to 1M tokens (long context)	96.2%	Argon, 84.2%	+12.0 points

The long-context result is worth a closer look. GraphWalks asks a model to follow links across a huge document. A 96.2% score from 256,000 to one million tokens means Carbon keeps track of details across very long inputs, like a full codebase or a year of tickets.

Google also reports that Carbon climbed 89 points to fourth place on WebDev Arena, where people vote on which model built the better web page. Fourth place is good, but it isn't first.

Where Gemini 4 Carbon Falls Short

Carbon has clear weak spots, and they're in areas many teams care about.

• Using a computer. On OSWorld-2.1, which tests an AI working a desktop like a person, Claude Opus 5.5 scores 87.2% and Carbon 81.5%.
• Large software tasks. On FrontierSWE 2, GPT-6 Astra scores 65.5% and Carbon 61.2%.
• Coding agents. On the Artificial Analysis Coding Agent Index, Opus 5.5 running in Claude Code scores 62. Carbon, running in Google's Antigravity CLI, scores 59.
• Hallucinations. On AA-Omniscience, Carbon makes up answers more often than Argon: 11% against 4%.
• Speed and tokens. The Decoder reports that Carbon uses 2.3 times Argon's reasoning tokens, and some agent tasks take more than twice as long.

So if your main job is an agent that clicks through apps, or a coding agent working inside a large repo, Carbon isn't the clear pick. Opus 5.5 or Astra may still be better there. You'd need to test on your own tasks to know.

Why Cyber Defense Is the Headline

Google built its launch around security, and the numbers show why. Carbon covers the three main steps of a defender's job:

• Finding flaws: 88.2% on SEC-bench Pro, which tests whether a model can find real flaws in software.
• Confirming them: 54.8% on ExploitGym, which checks whether a found flaw can actually be triggered.
• Fixing them: 71.0% on CWE-bench, which tests patches.

The ExploitGym gap is the largest. Carbon beats the next model by more than 12 points. For defenders, that step matters: confirming that a flaw is real is what turns a long list of warnings into a short list of urgent fixes.

Google also quotes its own Chrome team. It says Carbon "performed more than 10X better" than another lab's large frontier model at writing high-quality patches. Google doesn't name the other model or share the test, so treat this as a claim, not a result.

For security teams, the practical promise is simple. A model that finds flaws, checks them and drafts patches could help small teams keep up with a growing backlog. But every patch still needs a human review before it ships, because a fix that looks right can break other code or hide the flaw instead of removing it.

Who Can Use Gemini 4 Carbon Now?

Very few people. Google is rolling it out in stages, starting with the people best placed to use its cyber skills for defense, and widening access after that with no fixed dates yet.

1. This week: a small group of cyber defenders.
2. Next: more trusted partners through Google DeepMind's Fairwind Program.
3. Later: developers, businesses and consumers, "as soon as possible."

There's no date yet for the Gemini app, Google AI Studio or the Gemini API. Google is also part of the US government's voluntary testing process for new models before release, which adds another step that can take weeks before wider access opens. Until those steps finish, most teams can only plan.

Why Labs Ship to Defenders First

This isn't new for Google. It released Gemini 4 Argon to cyber defenders first too, on September 30. Argon reached the paid API and Gemini app about two weeks later, on October 13.

The logic is simple. A model that's good at finding flaws helps both sides. Giving defenders a head start lets them find and fix problems before attackers get the same tool.

Other labs now do the same with their strongest models. We looked at the incidents behind that caution in our piece on AI agent incidents at the UN Security Council. If your team builds agents that touch code or systems, our guide to agentic AI security covers the basic controls.

The Hallucination Trade-Off

Here's the detail most launch posts skip. Carbon knows more than Argon, but it also guesses more.

On AA-Omniscience, a knowledge test from Artificial Analysis, Carbon answered 58% of questions correctly. Argon answered 48%. But Carbon's hallucination rate is 11%, against 4% for Argon. That rate counts how often a model gives a wrong answer when it could have said it didn't know.

AA-Omniscience	Gemini 4 Carbon	Gemini 4 Argon
Correct answers (higher is better)	58%	48%
Hallucination rate (lower is better)	11%	4%

Both numbers are good next to most rivals. Carbon's rate is still the second-lowest of all models tested, behind only Argon. But for some jobs, the gap matters a lot. Context decides.

Think about where a wrong answer costs more than no answer:

• Favor Argon's caution for legal research, medical notes, financial reports and compliance checks, where "I don't know" beats a confident mistake.
• Favor Carbon's knowledge for coding and research help, where a person checks the output anyway.
• Test both when you aren't sure, using questions where you already know the right answer.

What Will Gemini 4 Carbon Cost?

Google has shared prices, even though the API isn't open yet. They match Argon's.

Price per million tokens	Introductory	Regular
Input	$2.00	$4.00
Output	$10.00	$20.00
Cached input (95% off)	$0.10	$0.20

Google hasn't said when the introductory price ends. Argon's launch price runs until the end of November, but that date may not apply to Carbon. Plan your budget on the regular price, and treat the launch price as a bonus.

Artificial Analysis also measures what it costs to run its whole index at regular prices. Carbon costs $7.76 per task, against $10.04 for Claude Opus 5.5 and $6.25 for GPT-6 Astra. By our math, that makes Carbon about 23% cheaper than Opus 5.5 per task and about 24% more expensive than Astra, which puts it in the middle of the three on cost.

Price per token is only half the story. Carbon thinks longer than Argon, so it uses more tokens on the same job. Our guide to token efficiency explains why tokens per task drive the bill more than the price list does.

A Worked Cost Example

Here's a rough monthly estimate. It's our own math, based on the prices above, not a quote from Google.

Say your app sends 50 million input tokens and gets back 10 million output tokens each month. Many apps reuse the same long system prompt or documents, so assume 80% of the input can be cached.

Scenario	Input cost	Output cost	Monthly total
Introductory price, no caching	$100	$100	$200
Regular price, no caching	$200	$200	$400
Regular price, 80% of input cached	$48	$200	$248

Two lessons stand out. First, the end of the launch price doubles the bill if nothing else changes. Second, output tokens drive most of the cost once you cache your input. Since Carbon writes more and thinks longer than Argon, watch output volume closely in any pilot.

Two Weeks After Argon: Google's Fast Pace

Google shipped Carbon just two weeks after Argon became widely available, and four weeks after Argon's first announcement. That's a very short gap between two top models from one lab.

Date (2026)	Step
September 30	Gemini 4 Argon announced, first for cyber defenders
October 13	Argon reaches the paid Gemini API and the Gemini app
October 28	Gemini 4 Carbon announced, first for cyber defenders
Not announced	Carbon in the Gemini API and the Gemini app

The fast pace is good news for buyers, since prices hold while scores climb. There's a catch. But it also creates work. Every new model means new tests, new prompts and new cost checks.

The lesson for builders is to avoid hard-wiring one model into your product:

• Keep the model name in config, so a switch is a one-line change.
• Keep a test set you can rerun in an hour, so you can compare any new release against your current model on exactly the same tasks, with the same scoring, before you change anything in production.
• Track cost per task for every model you try.

Then a new release becomes a quick check you can finish in an afternoon, not a rebuild that ties up your whole team for weeks.

What Benchmarks Don't Tell You

Launch tables are useful, but they have limits. Keep these in mind when you read Carbon's.

• Most numbers come from Google. Only the Artificial Analysis scores are independent so far, and even its own page for Carbon wasn't fully live when the first news reports went out.
• Many leads are small. Several rows in Google's table differ by less than one point, which is within the range where a small change in test setup can change the winner.
• The cyber scores have no safety filters. Google tested the raw model. The product you get may refuse some of the same tasks.
• Your tasks aren't in the table. A model that wins on public tests can still lose on your documents, your code and your users' questions.

None of this makes Carbon weak. It means the table is a starting point, not a buying decision.

A Simple Example: Choosing a Model for a Code Review Agent

Here's a made-up example to show how to use these numbers. It isn't a real case.

A software company runs an agent that reviews every pull request for security problems. Today it uses Claude Opus 5.5. The team wants to know whether it should switch to Carbon when access opens, and what that switch would really cost in time and money.

The benchmarks point both ways. Carbon's cyber scores suggest it may spot more flaws. But Opus 5.5 leads the coding agent index, and Carbon's higher hallucination rate could mean more false alarms.

So the team builds a test set before access opens. It collects 200 old pull requests where the real flaws are known. When it gets access, it runs both models on the same set and counts three things: flaws found, false alarms and cost per review. The answer comes from that test, not from a launch chart.

Should You Wait for Gemini 4 Carbon?

For most teams, no. You can't use Carbon yet, and there's no date for the API. Waiting on a model with no release date can stall a project for months.

Here's a simple way to decide:

Your situation	What to do now
You run a security team	Ask about the Fairwind Program; Carbon is built for your work
You already use Gemini 4 Argon	Keep building; plan a quick switch test when Carbon reaches the API
You build computer-use agents	Stay with the current leader on OSWorld; recheck when Carbon opens
Wrong answers are costly in your app	Compare hallucination rates on your own data before any switch
You're starting a new project	Build on an available model, and keep the model swappable

How to Get Ready Before Access Opens

You can do useful work now, even without access:

• Build your own test set. Collect 50 to 200 real tasks from your own work, each with a known good answer, so you can score any model the same way every time. Our guide to AI agent evaluation shows how.
• Track cost per task, not price per token. Log the tokens in and out for each job on your current model, so you know your real cost per task before any new model arrives.
• Make the model swappable. Put the model name and settings in config, not in code.
• Set a hallucination budget. Decide how many wrong answers per hundred your app can live with, and which kinds of wrong answers must never reach a customer at all.
• Plan for caching. Find the long, repeated parts of your prompts, like system rules and reference documents, so you can cache them from day one and cut input costs by up to 95%.

When Carbon opens, you'll be able to test it in a day instead of a month.

What to Watch Next

• API and Gemini app dates, and whether paid API users get Carbon before consumers.
• The end of the launch price, which will double the cost per token.
• Full independent results from Artificial Analysis, LMArena and other testers, especially on coding agents and computer use, where Carbon trails its two closest rivals today.
• Carbon's safety report, including how the guarded product scores on cyber tests.
• Rival replies from Anthropic and OpenAI, who now trail by one and two points on the index.

Gemini 4 Carbon: Our Take

Gemini 4 Carbon is a strong release. It takes first place on the main independent index, makes a big jump over Argon and posts standout cyber defense scores. Its long-context results look especially useful for teams working with huge codebases and document sets.

But its lead is one point, most of its numbers come from Google and almost no one can use it yet. It also trails rivals on computer use and coding agents, and it guesses more often than Argon. For most teams, the right move is to keep building on what's available, prepare a test set and switch only when your own results say so.

How Van Data Team Helps Teams Test New Models

We help companies choose and switch AI models with clear tests, not launch-day hype. That includes custom test sets, cost-per-task tracking and model-swappable agent designs, so a new release like Carbon becomes a quick check instead of a rebuild.

If you're comparing models now, start with our look at Claude Opus 5.5 benchmarks and our review of GPT-6 Astra. Then get in touch for a quick review of your setup.

Sources: Google's Gemini 4 Carbon announcement, The Decoder, Trending Topics, 9to5Google and CellCog.