Google DeepMind announced on November 10, 2026 that Gemini 4 Carbon, the latest addition to its Gemini 4 frontier model family, is now available to developers, enterprises, scientists, and consumers. In the announcement on the Google DeepMind blog, Koray Kavukcuoglu, SVP of Google DeepMind and Chief AI Architect at Google, described Carbon as the company's strongest model, with frontier performance in cybersecurity capabilities and long-horizon agentic workflows across complex knowledge work, software engineering, and scientific reasoning.

Two weeks earlier, Google had released Carbon to a group of cybersecurity defenders through its Fairwind Program. Carbon arrives on the heels of Gemini 4 Argon, which Google says remains the ideal choice for the majority of software engineering and knowledge work. Carbon is built for tasks that test the limits of a model's intelligence — complex, multi-step coding and debugging, and open-ended scientific research and discovery — using more tokens than Argon to explore thoroughly, reason deeply, and run extended verification loops.

Across independent benchmarks designed to test model performance in real workflows, Google said Carbon pairs frontier accuracy with practical production economics. It ranks #1 on CursorBench 4.0, Perplexity's WANDR, and Zapier's AutomationBench at more than 2X lower cost per task compared to the next best external model.

To apply Carbon to larger, more complex projects, Google is previewing /swarm in Google Antigravity, as detailed in the Antigravity announcement. The feature turns a single prompt into a coordinated team of agents that can be tracked in real time. Developers can assign Carbon to the roles that demand the deepest reasoning, such as architectural design, coordination, and auditing, while pairing it with faster, lighter models across parallel isolated workspaces and adversarial verifiers that test every change.

Gemini 4 Carbon is available via the API in Google AI Studio and Gemini for business to all paying developers and enterprises, at the same introductory price as Gemini 4 Argon: $2 per million input tokens and $10 per million output tokens, according to Google's pricing documentation. A footnote in the announcement states that after the introductory period expires on November 30, 2026, the price of $4 per million input tokens and $20 per million output tokens will apply; the introductory rate applies only to the Carbon model intended for public access. The model is also rolling out to all Google AI Ultra subscribers in Google Antigravity, the Gemini app, and AI Mode in Search.

Higher-Precision Knowledge Work, Artifact Generation, and Data Analysis

Across legal, finance, and enterprise operations, Google said Gemini 4 Carbon combines frontier visual understanding, reasoning, and exhaustive retrieval to synthesize grounded insights across large sets of dense documents and data. The model follows strict schemas and formatting to generate polished and precise spreadsheets, presentations, and reports.

Because existing benchmarks do not adequately capture how agents reason natively over dense files containing charts, images, blueprints, schematics, and tables, Google partnered with Artificial Analysis to launch the new Multimodal Knowledge Agent (MKA) benchmark, which tests a model's multimodal performance in agentic workflows. Carbon ranks #2 on the benchmark while costing more than 6X less in average cost per task than the top-performing model.

Google also demonstrated Carbon's document-handling capabilities through multi-agent builds in Google Antigravity. A team of Gemini 4 Carbon agents turned historical photos and engineering plates into an interactive Three.js 3D exhibition of the Eiffel Tower's 1887–1889 construction from a single prompt. Working from seven public City Council planning PDFs (206 extracted engineering drawing views) and a single prompt, a multi-agent Carbon team built an interactive 906K-triangle Three.js reconstruction of Google's Platform 37 office across 10 overnight loops. In another demonstration, Carbon built an interactive multiscale textile visualizer based on an academic paper, modeling a virtual-fiber finite element framework with interactive 3D yarn meshes, 110-fiber polar tracking, and dynamic stress-strain curves with minimal prompting.

In pre-release evaluations, Carbon matched the performance of the best model tested on deep financial research in Hebbia's E2E Financial Agentic evaluation and ranked at the top out of 23 frontier model configurations on Balyasny Asset Management's General Financial Intelligence benchmark, which spans legal expertise, agentic tool use and execution, coding and data engineering, and perception and context understanding.

Improved Agentic Coding, Full-Stack Development, and 3D and UI Design

Carbon performs exceptionally well on engineering tasks that require multi-agent collaboration, such as multi-package migrations, cross-subsystem root-cause investigations, and complex architectural redesigns. For web app development, Google said it runs build-and-verify loops, recovers from errors, and checks both its code and the rendered UI to produce complete, working applications. Stronger visual design, spatial reasoning, and attention to environmental detail also carry over to 3D game development and creative coding.

Google showcased several builds powered by the /swarm feature in Google Antigravity. Carbon created an open-world 3D game in which players explore Google's Mountain View campus as a pelican riding a GBike; the system coordinated subagents to model 3D assets in Blender from Omni and Nano Banana references, build the browser game in Three.js with WebGL shaders, and pair procedural Web Audio sound effects with Lyria music. A playable version of the game is hosted in AI Studio.

Paired with improved front-end taste and motion design skills in Google Antigravity, Carbon reimagined the Google Merchandise Store in two prompts, proposing five art direction concepts and generating all-new product imagery with Nano Banana 2.1. The live app and the associated Gemini front end design skill are both publicly available.

Starting from a video reference generated with Gemini Omni in Google Antigravity, Carbon produced a cinematic multi-shot underwater Atlantis scene. The pipeline modeled procedural ruins, volumetric caustic lighting, and water simulation in Blender before converting the full environment to Three.js for real-time browser rendering.

Using a multi-agent loop in Google Antigravity, Carbon also reconstructed a playable c. 1780 Stein-Schule fortepiano with 61-key escapement physics from CAD drawings and X-ray radiographs, and placed it inside a 3D model of the Royal Conservatory of Music's Koerner Hall in Toronto with seat-specific spatial acoustics. The interactive instrument is available as a live app.

Frontier Cybersecurity Capabilities

Gemini 4 Carbon for cyber delivers Google's strongest cybersecurity capabilities to date, giving security teams the end-to-end intelligence needed to protect critical software. When accessed through the Fairwind Program, Carbon leads on key benchmarks across finding vulnerabilities (SEC-bench Pro), exploiting them (ExploitGym), and patching them (CWE-bench v1) — inspecting software, tracing how flaws can be chained together across systems, and generating verified root-cause patches.

Early testers inside and outside Google are already seeing strong results with Carbon for cyber in practice. In Project Fortify, Google's autonomous system spanning scanning, triage, and remediation, Carbon hit 100% recall on scanning and 95% precision on triage on Google's internal benchmark. On open-source code repository testing, Google reports an over 25% relative improvement on Argon's already strong recall rate, surfacing previously undetected, real-world logic flaws. Bringing these gains across Google — one of the world's largest users of open-source software — helps secure both the company's own systems and the broader ecosystem, Google said.

Google's Cloud Vulnerability Research (CVR) team recently used Carbon to prove that two bugs in a popular open-source software were in fact exploitable vulnerabilities, comparable in severity to the recent PHP2Shell exploit. By using Carbon to build a proof of concept, CVR helped maintainers re-classify the bug as critical and swiftly issue a patch. Details will be shared once the responsible disclosure process completes.

As part of Wiz's Scan for Good program, the team used Carbon to assess vulnerabilities in public digital infrastructure. While scanning two major SaaS providers, the model uncovered 0-day vulnerabilities in under two hours, compressing weeks of vulnerability research into a single afternoon. The team partnered with the impacted organizations to remediate in record time, helping to mitigate potential impact on tens of thousands of companies.

Stronger Scientific and Mathematical Reasoning

Gemini 4 Carbon shows strongly improved performance in reasoning across physics, math, and life sciences, synthesizing experimental data, testing hypotheses, and reasoning through multi-step domain problems. For drug toxicity research company Axiom Bio, Carbon is now the leading model in performance for translational science, finding novel mechanisms of toxicity the company had not previously identified. Axiom is now moving its production scientific analyses and agents to both Carbon and Argon to unlock the creation of safer drugs.

Researchers across Google have been using Carbon in Google Antigravity to accelerate their work:

• Code verification: Researchers built a new agentic framework for formal verification called AlphaProof Code and applied it to a major security challenge: mathematically proving the actual code underlying the cryptographic protocol of Signal. Using Carbon alongside other models, the researchers translated security goals into mathematical theorems. AlphaProof Code agents then used Carbon to generate millions of lines of code in Lean, a computer language for writing mathematical proofs, helping them prove key security properties in weeks — a task that historically takes expert teams years. Google said the result indicates Carbon's potential to unlock scalable formal verification, long considered a gold-standard approach to building secure software.
• Enzyme assay development: Provided with only lab notebooks and raw data, Carbon went beyond standard analysis to surface hard-to-interpret data anomalies, which it used to diagnose subtle wet lab experimental artifacts such as incomplete automated mixing in deep wells, leading to substantially improved reproducibility. Carbon also improved methods to deconvolute chromatography peaks and caught trace sample carryover in research instruments, significantly accelerating the development of new assays and tightening the feedback loop between computation and the bench.
• Protein structure prediction: Structure prediction models like AlphaFold 3 use Multiple Sequence Alignments (MSAs) of evolutionary relatives to help infer how amino acid chains fold, and as a result they often struggle on understudied, viral, microbial, and eukaryotic proteins that have very few matches in standard databases. Working in a multi-agent setup, Google's researchers used Gemini 4 Carbon to autonomously discover remote homologs across petabase-scale unannotated sequence archives, environmental and viral sequence catalogs, and 3D structure databases. Across 7 low-homolog protein families from the CASP17 blind test set, Carbon increased the number of homologs retrieved by 40-fold on average, greatly increasing the information available to structure prediction models without the need for retraining. While broader benchmarking is still underway, addressing this upstream search bottleneck potentially offers a promising path toward modeling hard-to-characterize regions of the proteome.

Four weeks before the announcement, Google opened applications to give 10,000 scientists access to Argon in Google Antigravity free of charge for six months to accelerate the pace of their research, and thousands of scientists have already been onboarded. With Carbon's release, Google is expanding access to Carbon for current and future participants, and the program will continue providing access to Google's latest frontier models as they become available. Applications remain open through Google's application form, and onboarding continues on a weekly rolling basis, subject to eligibility, capacity, and selection.

Progress in Model Safety and Security

Google said that with each new model release it strengthens safety and security mitigations, agentic misuse prevention, model alignment, and harm reduction proportionately with model capability. Because Gemini 4 Carbon introduces stronger cyber capabilities, the company built on Argon's safety and security stack to fortify robustness to misuse and prompt injection.

On the misuse side, Google said it strengthened safeguards to help ensure stronger defensive reasoning does not translate into offensive risk. In simulated cyberattack testing environments seeded with ExploitBench and CyScenarioBench, Carbon blocked 99% of attack trajectories from Google's adaptive red-teaming agent, which can iteratively revisit its attack plans after each block. The Carbon safeguards lowered the per-attempt breach rate to 0.8%, down from 3.1% on Argon, and lowered the automated cyberattack query success rate to 2.17% in Gray Swan's testing, compared to 9.86% on Argon. External testing by Trajectory Labs found zero new universal jailbreaks across 132 hours and more than 13,000 requests.

On alignment, Google reported significant improvements measured by monitor flag rates across the model's lifecycle, along with reductions in data fabrication. On Google's 375-task agentic honesty benchmark, Carbon passed 92% of tasks, compared with 70% for Argon — declining to fill in missing experimental data 98% of the time and flagging methodological flaws 68% of the time.

On prompt injection, Google shared two weeks before the announcement that Carbon achieved a 0% prompt injection attack success rate on the Gray Swan IPI benchmark, marking the first time that milestone had been reached. At the same time, Google acknowledged that prompt injection robustness remains an area for continued improvement: its advanced automated red teaming has shown that models could be further improved against sophisticated attacks in some distributions, providing data the company says it will use to continuously advance its models' security. Additional detail is available in the full Gemini 4 Carbon Safety and Security report (PDF) and in Google's related safety and security post.

Getting Started With Gemini 4 Carbon

Starting November 10, Carbon joins Argon in Google's family of frontier models across its products:

• Developers: Build with the Gemini API paid tier in AI Studio and Android Studio; Google AI Ultra subscribers can try it in Google Antigravity and AI Studio.
• Enterprises: Access it in Gemini for business via the API or the model dropdown, in Google Antigravity, or in CodeMender.
• Everyday users: Try Carbon starting today in the Gemini app and AI Mode in Search with a Google AI Ultra subscription.
• Scientists and researchers: Apply for a chance to get access to both Argon and Carbon via Google Antigravity's highest usage tier free of charge for 6 months, with onboarding on a rolling basis, subject to eligibility, capacity, and selection.
• Cybersecurity defenders: Apply to join the Fairwind Program to get access to Carbon for cyber on a rolling basis.

More information is available in the Gemini 4 Carbon model card (PDF), along with Google's updated Gemini 4 developer guide and documentation, which cover best practices for migrating workflows to the model.

About the Author

Koray Kavukcuoglu is SVP of Google DeepMind and Chief AI Architect at Google, where he leads the development of Google DeepMind's state-of-the-art generative AI models and their integration into Google's products at scale. Prior to his current role, he was VP of Research at Google DeepMind, where he founded the deep learning team and his team pioneered major research breakthroughs such as DQN (Deep Q-Network) and WaveNet.