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Ahsan Nadeem
Ahsan Nadeem

Posted on Originally published at znovastudio.com

Gemini 4 Carbon: What It Really Costs per Task, What the $2 Price Leaves Out, and Who Can Use It Yet

#ai #google #llm #machinelearning

Gemini 4 Carbon, announced by Google DeepMind on 28 October 2026, tops Artificial Analysis's Intelligence Index at 59. The intro price is $2/$10 per million input/output tokens, but the number to plan around is Artificial Analysis's $7.76 per task at the $4/$20 list price. Only a subset of cyber defenders can use it, and there's no date for anyone else.

That's the short answer. The surprise is where the $7.76 goes: by eesel's reading of Artificial Analysis's breakdown, about 68% is input (mostly cache writes and reads), not output. So the "$10 per million output tokens" headline tells you very little about what an agent workload will cost.

Can you use it yet? Only if you're among the cyber defenders Google is vetting through its Fairwind Program, where governments, critical infrastructure and core technology platforms come first. If not, the checklist near the end covers what to do now.

If you're weighing which model to build on, choosing and wiring in the right one is the core of our LLM integration work.

What is Gemini 4 Carbon?

Gemini 4 Carbon is a large language model from Google DeepMind, announced in a Google blog post on 28 October 2026 by Koray Kavukcuoglu. It arrived two weeks after Gemini 4 Argon. Google calls it "our strongest model with state-of-the-art cybersecurity and long horizon capabilities", meaning work that runs for a long time without a person stepping in.

Google's headline numbers:

  • #1 on the Artificial Analysis Intelligence Index v5.0, at 59.
  • Cyber benchmarks: 88.2% on SEC-bench Pro (finding vulnerabilities), 54.8% on ExploitGym (exploiting them) and 71.0% on CWE-bench (patching them).
  • WebDev Arena: up 89 Elo to fourth place (1768).
  • Long runs: Google says its teams "have seen Carbon run for days to persist through dead ends" when root-causing and patching bugs in large codebases.

Google doesn't state a context window. Artificial Analysis lists it at 1 million tokens.

When can you use Gemini 4 Carbon? Release date, API and the Fairwind Program

Not yet, unless you defend critical systems. Google says that "starting this week" Carbon goes to "a subset of cyber defenders", with access gradually extended to more trusted partners through its Fairwind Program. For developers, businesses and consumers, Google says "as soon as possible" and gives no date.

Fairwind is selective. It prioritises governments, critical infrastructure (healthcare, telecoms, energy, financial networks) and core technology platforms, vets applicants with background checks, and limits access to internal security teams. Google says it has over 650 partners.

As of 29 October, the Fairwind page still describes access to Argon, and Carbon isn't on the Gemini API pricing page.

If you're a typical business, you can't call it today.

Gemini 4 Carbon pricing: what $2 and $10 per million tokens really means

Google's announcement gives an introductory price of $2 per million input tokens and $10 per million output tokens, with cached input at "95% off input token price". A footnote says that after the introductory period, "$4 per 1M input tokens and $20 per 1M output tokens will apply".

There's no end date for Carbon's introductory price. For comparison, Google's pricing page shows Argon's intro price running through 30 November 2026 and doubling on 1 December. The Decoder reports that Carbon's discount "runs through the end of 2026", but Google itself hasn't said so, so treat the date as unknown.

The practical rule is simple: budget at $4/$20. If a project only works at the intro price, it doesn't work.

What does Gemini 4 Carbon cost per task?

Artificial Analysis measures what it costs to run each model through its whole index. For Carbon, at the $4/$20 list price, that's $7.76 per task. That ranks 45th of 51 models, among the most expensive.

Running the full index took Carbon 200 million output tokens, against a median of 64 million.

That's heavy next to the median, but lighter than Claude Opus 5.5. In its post on X, Artificial Analysis said Carbon used about 123,000 output tokens per task, 29% fewer than Opus 5.5 (173,000), and put it about 23% cheaper per task than Opus 5.5. The Decoder reports that Carbon generates 2.3 times as many reasoning tokens as Argon.

Here's where the $7.76 goes, according to eesel's reading of Artificial Analysis's breakdown:

Part of the bill Cost per task (list price) Share
Cache writes $3.21 41%
Cache reads $2.04 26%
Fresh input $0.05 1%
Thinking (reasoning output) $1.44 19%
Answer (visible output) $1.01 13%
Input side $5.30 68%
Output side $2.46 32%

The largest single item is writing context into the cache, not generating answers. An agent re-reads and re-stores a lot of context on a long task, and that's what you pay for.

Vals AI's index tells a similar story from another angle: The Decoder reports Carbon cost $10.51 per test and took about 48 minutes, with legal research tasks averaging more than 50 minutes each.

A worked illustration: cost per task at intro vs list price

This is our own arithmetic on the figures above, not a measured workload. It assumes your tasks look like Artificial Analysis's index tasks, which yours probably won't.

  • At list price, 1,000 tasks a month costs about $7,760.
  • At the intro price, assuming every rate (cache writes included) is half the list rate, the same 1,000 tasks would cost about $3,880. When the intro ends, the bill doubles with no change on your side.
  • Halve the output tokens (thinking plus answer, $2.46 in the table) and you save about $1.23 per task, roughly 16%.
  • Halve the cache writes ($3.21) and you save about $1.60 per task, roughly 21%.

The point: trimming what goes into context (shorter system prompts, fewer tool results stuffed back in, stable prefixes that cache well) moves the bill more than squeezing the answer length. Prompt and context design matter more than the output price.

What the headline benchmarks leave out

The launch coverage repeats the same few numbers. These are the ones it tends to skip.

  • More confident mistakes than Argon. The Decoder, reading Artificial Analysis's AA-Omniscience results, reports Carbon's accuracy at 58% (Argon 48%) but its hallucination rate at 11% against Argon's 4%. That's still the second-lowest in the ranking, but it's worse than its own sibling.
  • A very slow first token: not for chat yet. Artificial Analysis measured 97.48 seconds to the first token, against a median of 3.58, then 99.8 tokens per second. These were measured on Google's API during limited access and may change at general release.
  • Long runtimes. The Decoder reports finance and tax agent tasks on Vals AI took more than twice as long as Argon.
  • Cyber scores without guardrails. Google says its cyber benchmarks were "evaluated without guardrails". ExploitGym is the same benchmark at the centre of the OpenAI Hugging Face incident, where agents broke out of their test environment looking for the benchmark's answers. Scores measured without safeguards tell you about raw capability, not how the released product will behave.
  • Internal claims you can't check. None of these come with a methodology: Google says Carbon is "more than 2x" better than Argon at generating secure code, makes about 2x fewer unsupported claims, and cut high-severity misalignment alerts by almost 80% in internal coding environments. Its Chrome team says it's "more than 10X better than another lab's large frontier model" at patches, without naming the lab.
  • A prompt injection test that's maxed out. Carbon scored a 0.0% attack success rate on Gray Swan's indirect prompt injection test (Argon 0.7%). Google itself says the test looks saturated, and The Decoder notes that doesn't mean the problem is solved. Our roundup of prompt injection attack examples shows why you still need controls around any agent that reads untrusted input.

Gemini 4 Carbon vs Argon, GPT-6 Astra and Claude Opus 5.5

Scores and costs below are Artificial Analysis Intelligence Index figures, taken from Artificial Analysis's Carbon page and from eesel's copy of Artificial Analysis's table. Cost per task is at each model's standard price.

Model Intelligence Index Cost per index task
Gemini 4 Carbon 59 $7.76
Claude Opus 5.5 (max) 58.4 $10.03
GPT-6 Astra (max) 56.9 $6.24
GPT-6.1 Sol (max) 54.5 $1.27
Gemini 4 Argon (high) 50.4 $7.68

Artificial Analysis's own post gives Opus 5.5 as $10.04 and GPT-6 Astra as $6.25, a cent off eesel's figures.

Look at the GPT-6.1 Sol row. GPT-6.1 Sol scores about 92% of Carbon's index score for about 16% of Carbon's cost per task. That doesn't make it the better model for every job; an index average hides big differences on specific tasks. But if your work doesn't need the very top of the table, the price gap is hard to ignore.

On other measures, Carbon isn't always first:

  • Coding agents: Artificial Analysis's Coding Agent Index puts Carbon at 59, Claude Opus 5.5 at 62 and Argon at 50 (via The Decoder).
  • Agent Arena: Carbon is second, behind Opus 5.5 Max, with a median task cost of $1.80 (via The Decoder).
  • Knowledge work: on AA-Briefcase, Carbon scores 1,609 Elo, behind Kimi K3.1 Pro (1,678), Muse Wonder (1,659) and Grok 4.7 (1,638) (via The Decoder).
  • Vals AI Index v2.2: Claude Sonnet 5.5 70.85%, Carbon 70.36%, Claude Opus 5.5 70.18%, Argon 69.24%, close to level (via The Decoder).

Which jobs Carbon suits, and which it doesn't

Based on what Google and the independent testers report, Carbon looks built for:

  • Long, unattended security work: finding, root-causing and patching vulnerabilities across large codebases.
  • Agent tasks that run for minutes or hours, where nobody is watching the first token arrive.
  • Teams that already qualify for Fairwind and have security staff to supervise it.

It's a poor fit, at least for now, for:

  • Chat, customer support or anything a person waits on, given a time to first token near 97 seconds in testing.
  • High-volume, cost-sensitive work, where cheaper models score close behind.
  • Answers that must be factually right first time, given the higher hallucination rate than Argon.
  • Anything you need to ship this quarter, because you can't get access.

What should your business do now?

You can't use Carbon yet, so the work now is getting ready to judge it properly when you can.

  1. Don't plan around access you don't have. No date means no roadmap item. Build on models you can call today.
  2. Test on your own tasks. Benchmarks are averages of someone else's work. Build a small set of your real tasks with known good answers; our guide to RAG chatbot evaluation shows how to set one up.
  3. Measure cost per task, not per token. Log total spend and tokens for each completed task, split into cache writes, cache reads, fresh input and output. That's the number to compare across models.
  4. Design for caching. Keep system prompts and tool definitions stable so they cache, and keep what you feed back into context lean. Cache writes are the biggest slice of Carbon's bill.
  5. Budget at the list price. Use $4/$20 in every estimate, whatever the intro price is when you start.
  6. Keep a model-swap layer. Put model calls behind one interface so moving between Carbon, Argon, GPT-6.1 Sol or Opus 5.5 is a config change, not a rewrite.
  7. If you're not Fairwind-eligible, look at CodeMender. Google's Fairwind page says that if you're not eligible "you can still protect your code using CodeMender with publicly available models."

Want a second opinion on which model fits your workload and what it will really cost per task? Start with step 3 above: log cost per task for one workflow this week. Then, if you want a second pair of eyes, book a free consultation and we'll give you a straight answer, including telling you when a cheaper model is fine. It's the kind of decision our LLM integration service is built around.

Frequently asked questions

How much does Gemini 4 Carbon cost?

Google's introductory rate is $2 for every million input tokens and $10 for every million output tokens, and cached input gets a 95% discount. After the introductory period it rises to $4 and $20, and Google hasn't said when that happens. Per task, Artificial Analysis measured $7.76 per Intelligence Index task at the $4/$20 price, which is the more useful number for budgeting.

Is Gemini 4 Carbon available in the Gemini API?

Not as of 29 October 2026. Carbon isn't listed on Google's Gemini API pricing page, and access is limited to a subset of cyber defenders, and will gradually extend to more trusted partners through the Fairwind Program. Google says developers and businesses will get it "as soon as possible" but gives no date.

Is Gemini 4 Carbon better than Claude Opus 5.5 for coding?

Not clearly. Carbon scores slightly higher on Artificial Analysis's overall Intelligence Index (59 against 58.4), but Opus 5.5 leads Artificial Analysis's Coding Agent Index (62 against 59) and Arena's Agent Arena, according to The Decoder. Carbon is cheaper per index task ($7.76 against about $10), so the right choice depends on your tasks and budget.

What is the difference between Gemini 4 Carbon and Gemini 4 Argon?

Carbon launched two weeks after Argon and scores higher: 59 against 50.4 on Artificial Analysis's Intelligence Index, and 58% against 48% accuracy on AA-Omniscience. But it hallucinates more (11% against 4%), generates 2.3 times as many reasoning tokens and costs about the same per index task ($7.76 against $7.68). Argon is available in the Gemini API; Carbon isn't yet.

What is the context window of Gemini 4 Carbon?

Google hasn't stated one. Artificial Analysis lists Carbon's context window as 1 million tokens; Carbon is only in limited access, so check Google's documentation once the model is generally available.

Sources

Originally published on znovastudio.com.

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