Gemini 4 Carbon Is Callable Now, and the Price Doubles in Twenty Days

For twelve days, G​emini 4 Carbon was the best model almost nobody could call. G​oogle announced it on 28 October 2026 to a named cohort of cyber defenders and Fairwind Program partners, with no model ID, no endpoint and no rate card. On 10 November 2026 that changed: gemini-4-carbon is now a stable model on the paid G​emini API, in AI Studio and Android Studio, and for enterprises through G​emini for business. The part worth planning around is the calendar. It costs $2 per million input tokens and $10 per million output tokens until 30 November. On 1 December those numbers become $4 and $20 with nothing else about the model changing.

So this is a launch piece where the launch is real, and the useful information is not the marketing. It is the access matrix, the twenty-day discount, and the fact that the "#1 on the Artificial Analysis Intelligence Index" line from October now reads fourth on the same board.

Who can actually call it, and who still cannot

The 10 November post is unusually specific about channels, which makes it worth reading as an access table rather than an announcement. What opened:

• Paid G​emini API developers, through AI Studio and Android Studio, with G​oogle AI Ultra subscribers also able to reach it in Antigravity and AI Studio.

• Enterprises, through G​emini for business on either the API or the model dropdown, plus Antigravity and CodeMender.

• Consumers on G​oogle AI Ultra, in the G​emini app and AI Mode in Search.

• Scientists and researchers by application only, with Argon and Carbon in Antigravity's top tier, free for six months subject to eligibility and capacity.

• Cyber defenders, still through the Fairwind Program, which is where the whole rollout started.

What did not open is the free tier. G​oogle's pricing page is explicit: "Not available" for G​emini 4 Carbon, and every processed token is billed — including thinking tokens, and including tool system instructions when grounding is on. There is also no playground-grade ceiling mentioned anywhere in the docs; rate limits are not published on the model page. If you are budgeting a pilot, that is the one number you will have to discover by calling the API rather than by reading about it.

The twenty-day price, and the cliff behind it

G​oogle is running Carbon on the same introductory rate card it used for G​emini 4 Argon: $2 in and $10 out through 30 November, then $4 and $20 from 1 December 2026. The pattern is not new — Argon launched the same way — which is why the date matters more than the discount. A workload priced at the intro rate today is priced 100% higher on the same tokens three weeks from now.

The rest of the card is where teams get surprised:

• Cached input — $0.10 per million until 30 November, $0.20 after. At the current rate a cached token is 5% of a fresh one, and the multiplier holds after the change.

• Batch and Flex — half the standard rate, $1.00 in and $5.00 out now, $2.00 and $10.00 after 1 December. If your job tolerates latency, this is the single biggest lever on the bill.

• Priority — 1.8x standard, $3.60 in and $18.00 out now, $7.20 and $36.00 after.

• Contributor — $1.00 in, $5.00 out, $0.05 cached, invite-only preview. Not a general option.

• Long context — there is no length-based tier. Unlike G​emini 3.1 Pro, which steps up a price band past 200,000 input tokens, Carbon charges the same per-token rate whether you send 2,000 tokens or 900,000. That is a genuine advantage for large-codebase work and it is easy to miss because most vendors of 1M-token models do tier.

Caching is implicit only. Explicit context caching is not supported on Carbon, so you cannot pin a document and guarantee the cached rate the way you can on some other G​emini models. If your saving plan depended on explicit cache hits, it does not apply here.

The #1 claim, and what the board says four revisions later

G​oogle's 28 October post led with a first-place finish on the Artificial Analysis Intelligence Index. That was accurate for the board as it stood that day. It is not what the board says now, and this is the sort of drift that gets quoted for months after it stops being true.

Read the current listing and G​emini 4 Carbon at high effort scores 60 on Intelligence Index v5.0.1 — fourth of 74 models. Above it sit three configurations of Claude Fable 5.2, at 65, 62 and 62. Carbon's own effort dial moves the number too: medium lands at 58 and low at 54, which means a single "Carbon scores 60" line in a comparison is only true at high effort.

The vendor-reported numbers from the 10 November post, none of which have been independently reproduced, are a different and much louder set: first on CursorBench 4.0, on Perplexity's WANDR and on Zapier's AutomationBench, at what G​oogle describes as more than 2x lower cost per task than the next-best external model; second on a new Multimodal Knowledge Agent benchmark built with Artificial Analysis, at over 6x lower average cost per task than the top model; a tie for best on Hebbia's end-to-end financial evaluation; and top of 23 frontier configurations on Balyasny's GMI. Every one of those is G​oogle's own framing of a benchmark, and the cost-per-task advantage is a claim about a specific task mix, not a general price statement.

The cyber numbers follow the same pattern. On 28 October G​oogle published SEC-bench Pro at 88.2%, ExploitGym at 54.8% and CWE-bench at 71.0%. On 10 November it added Project Fortify at 100% scan recall and 95% triage precision on open-source code, a 25%-plus relative recall gain over Argon, and a 40-fold average increase in homologs retrieved across seven CASP17 protein families. Those are the claims that justify the model's existence for a security team. They are also unaudited by anyone outside G​oogle, and the model has been generally callable for one day.

What the independent board does say, and the number that matters

Artificial Analysis has measured Carbon rather than repeated G​oogle's chart, and its numbers are the ones to budget against:

• Cost per Intelligence Index task — $7.76 at standard pricing. That is not the cheapest way to buy intelligence; it is among the most expensive on the board, ranked 61st of 73 on that measure.

• Output tokens consumed on the index — 205 million, against a median of 72.8 million. Carbon is a verbose model, third from the bottom on verbosity rank. Per-token price understates what a Carbon workload actually costs when the model thinks for longer and writes more.

• Speed — 80 output tokens per second, with 66.85 seconds to the first answer token. Compared with the 12-second median for its tier, Carbon is a model you batch rather than a model you put behind a chat box.

• Context — 1M tokens, confirmed both by G​oogle's docs and by the third-party listing.

Put the verbosity and the price together and the arithmetic is unforgiving. A task that costs $7.76 on Carbon costs roughly a fifth of that on a cheap open-weight model — which is the comparison the next article in this set makes in detail. Carbon's case is not that it is efficient. It is that on cyber and long-horizon agent work there may be nothing else at the same capability.

What to do with the next twenty days

Three practical moves, in order of how much money they save.

First, benchmark on the intro rate and price the result on the December rate. Anything you decide in November using November numbers will look 100% better than it is. If a pilot shows a $400 monthly bill this month, the same traffic is $800 from 1 December at standard tier.

Second, decide whether you need Carbon or whether Argon will do. G​oogle says so itself in the announcement: Argon "remains the ideal choice for the majority of software engineering and knowledge work," and Carbon uses more tokens than Argon to get its answers. The two models share an identical rate card, which means the choice is purely about capability per token spent — and for most workloads the cheaper-per-answer model is Argon.

Third, keep the integration portable. This is where routing earns its keep, and it is worth being precise about what is and is not available today. G​emini 4 Carbon is reached through G​oogle's paid API; it is not in our catalogue, and nothing here should be read as claiming otherwise. G​emini 4 Argon is: google/gemini-4-argon sits on OrcaRouter at G​oogle's own $2 in / $10 out, with the same OpenAI-compatible surface as the other 200-plus models on the key. DeepSeek V4 Pro and Grok 4.7 — the two opponents in this batch — are also there, at $0.66/$1.98 and $2/$6 respectively. Because we pass provider list price through with 0% markup, a vendor rate change is live on our side the day it happens rather than after a repricing cycle — which is the specific thing that matters on 1 December.

That portability is the whole argument for not hard-coding one vendor's SDK in November. Carbon's access conditions have now changed twice in fourteen days: gated announcement, then paid API. The rate changes on 1 December. A month in which both the price and the door moved is not a month to build a single-vendor dependency.

What is still unknown

Two things, and they are the ones to watch rather than the benchmark table. Rate limits are unpublished, so nobody outside G​oogle knows what a production Carbon integration can actually sustain — the 28 October cohort was small enough that throughput was never tested publicly. And the consumer picture is Ultra-only: there is no free path to Carbon and no indication of when there will be. If the model follows Argon's arc, a cheaper sibling will absorb the volume and Carbon will stay the expensive specialist. That is the shape the pricing already implies.