Google made Gemini 4 Carbon broadly available on November 10, 2026, opening its latest frontier model to paying developers and enterprises after a limited cybersecurity release two weeks earlier through the Fairwind Program. Carbon is also rolling out to Google AI Ultra subscribers in Antigravity, the Gemini app and AI Mode in Search, according to Google’s launch announcement.
The production model ID is gemini-4-carbon. Its defining technical feature is a shared 1,048,576-token budget for input and output, with long-decode support that lets generation continue across multiple segments.
Google is initially charging the same API rates as Gemini 4 Argon: $2 per million input tokens and $10 per million output tokens through November 30. The scheduled rates afterward are twice as high. That makes the launch window useful for evaluation, but a poor baseline for estimating ongoing production costs.
What Is Generally Available Now
Google lists Carbon as generally available through the Gemini API paid tier, accessible through Google AI Studio, and Gemini for business. The Google Cloud model documentation confirms the GA status and November 10 release date for gemini-4-carbon.
The documented feature set includes function calling, structured output, code execution, URL context, computer use and batch inference. Standard and priority pay-as-you-go access are supported, alongside provisioned throughput. Carbon accepts text, image, audio and video inputs, but its output modality is text.
The Ultra rollout is a separate availability track. Google says it is rolling out to subscribers, without specifying when every account will receive access. Developers can therefore distinguish an available paid API model from a consumer rollout that may still be reaching accounts.
Two other access distinctions matter: Antigravity’s /swarm feature remains a preview, while the specialized Carbon cybersecurity variant remains allowlisted. Neither should be treated as unrestricted access simply because the main Carbon model is GA.
The Million-Token Limit Is Shared
Carbon’s headline number needs careful reading. The model has one unified token allowance covering the prompt and generated output, including thinking, function calls and the final answer. It is not a million-token input window plus another million tokens of generation.
Google documents the following limits across its supported API paths:
| Carbon limit | Documented allowance |
|---|---|
| Combined input and output | 1,048,576 tokens |
| One foreground segment, streaming | Up to 65,536 output tokens |
| One foreground segment, non-streaming | Up to 32,768 output tokens |
| Extended output using long decodes | Up to 1,048,576 tokens minus input |
These figures come from the Carbon model documentation. The smaller foreground limits explain why a nominally million-token model still needs continuation handling.
For example, an input consuming 262,144 tokens leaves a theoretical maximum of 786,432 output tokens. That remainder must accommodate thinking and function calls as well as the answer users see. A document-heavy task can therefore leave substantially less room for generation than the headline capacity suggests.
This is a useful distinction for repository migrations, research synthesis and large artifact generation. Adding more source material can improve the information available to the model, but it also reduces the remaining generation allowance. My view is that applications should reserve output capacity explicitly instead of filling the context window first and hoping the answer fits.
Long Decodes Require Continuation Handling
Google supports long decodes through the Interactions API and GenerateContent API, with an important version caveat: its documentation says continuation_token is supported only in v1beta1. The Google Gen AI SDK defaults to that version.
The documentation describes three ways to handle extended generation:
- Google Gen AI SDK and Agent Studio: Automatically handle continuation tokens across segments.
- Interactions API: Supports
background=truefor background generation. - Custom continuation loops: Pass the returned
continuation_tokento continue generation.
A custom client that stops after its first foreground segment will not receive the full long decode. Likewise, an application pinned to another API version needs to check continuation support before relying on it.
I’d make continuation behavior an early migration test. A short-answer smoke test can succeed while leaving the application’s long-output path completely untested.
The larger output allowance is also not exclusive to Carbon. Google’s migration comparison lists a 1,048,576-token output limit for both Gemini 4 Argon and Carbon, versus 65,536 for models such as Gemini 3.8 Flash. Carbon’s launch should not be read as the first appearance of that capability across the entire Gemini family.
Settings That Change During Migration
Switching the model ID is only part of the work. Carbon changes several controls that existing Gemini applications may assume are configurable.
According to the model documentation:
- Thinking levels:
LOW,MEDIUMandHIGHare supported. The publicgemini-4-carbonmodel defaults toMEDIUM. - Unsupported thinking settings: Supplying
thinking_budgetor settingthinking_level="MINIMAL"returns an HTTP 400 validation error. - Sampling controls: Custom
temperature,top_pandtop_kvalues are ignored. Their fixed values are 1.0, 0.95 and 64, respectively. - Caching: Carbon supports implicit caching with a 4,096-token minimum and persistent caching enabled by default. Explicit context caching is unsupported.
The difference between rejected and ignored settings is consequential. An unsupported thinking configuration produces a visible failure. A custom temperature value can remain in a request without controlling sampling as the application expects.
For teams using a low temperature as part of a consistency strategy, successful requests alone are insufficient evidence that the old behavior carries over. Evaluate the resulting outputs and use supported mechanisms, such as structured output, where appropriate.
Caching deserves a separate review. A pipeline built around explicitly created cache resources cannot assume those resources transfer to Carbon. Implicit caching offers reuse without explicit cache management, but the documented minimum is an eligibility condition, not a promise that every repeated request receives a particular saving.
Carbon also lacks Gemini Live API support and fine-tuning. Those are feature-selection constraints, not settings that can be restored by changing a request parameter.
Introductory Pricing Ends November 30
Google’s announcement sets out two pricing periods for the public-access model:
| Pricing period | Per million input tokens | Per million output tokens |
|---|---|---|
| Introductory, through November 30, 2026 | $2 | $10 |
| Scheduled after November 30 | $4 | $20 |
The introductory rate matches Argon’s announced pricing. It does not mean a Carbon task will cost the same as an Argon task: Google says Carbon uses more tokens to explore problems, reason and perform extended verification.
Consider an illustrative workload consuming 200,000 input tokens and 800,000 output tokens. Applying the published rates gives:
- Introductory token charge: $0.40 for input plus $8 for output, totaling .
Frequently Asked Questions
4 questions
1What Is the Gemini 4 Carbon Model ID?
The generally available public model ID is gemini-4-carbon. The allowlisted cybersecurity variant uses gemini-4-carbon-cyber-permissive.
