Google DeepMind 发布 Gemini 4 Carbon 面向网络防御与智能体编程

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Home Editors Pick Agentic AIGoogle DeepMind Introduces Gemini 4 Carbon, Its Top Model for Cyber Defense...

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Google DeepMind Introduces Gemini 4 Carbon, Its Top Model for Cyber Defense and Coding

Asif Razzaq

October 28, 2026

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Google DeepMind has introduced Gemini 4 Carbon, its strongest model to date. Gemini 4 Carbon is a frontier model from Google built for long-horizon agentic coding and cybersecurity defense. It ranks #1 on the updated v5.0 Artificial Analysis Intelligence Index with a score of 59. For developers, the catch is access. Carbon starts with a vetted group of cyber defenders, not the public API.

TL;DR

Size: Parameter count and context window not disclosed. Its predecessor, Gemini 4 Argon, has a 1,048,576-token context.
Runs on: Google-hosted only. No open weights, no self-hosting, no quantized variants.
Performance: Leads 15 of 17 published benchmarks versus Argon, GPT-6 Astra, Opus 5.5 and Fable 5.1.
Best: 96.2% BFS F1 on GraphWalks (256K to 1M tokens), versus 84.2% for Argon.
Worst: 81.5% on OSWorld-2.1, trailing Claude Opus 5.5 at 87.2%.
Bottom line: Best: state-of-the-art vulnerability finding, exploitation and patching. Worst: you probably cannot call it yet.

What is Gemini 4 Carbon?

Carbon is the second Gemini 4 model, following Gemini 4 Argon. Google team states that the model targets hard, long-running engineering work. Internal teams saw Carbon run for days on single tasks. Examples include root-causing and patching subtle bugs across large codebases and low-level systems.

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Google also tuned Carbon for full-stack web development and multimodal understanding. On WebDev Arena, Carbon gained 89 Elo points to reach #4 at 1769.

How does Gemini 4 Carbon perform on benchmarks?

Google published a 17-row comparison in its launch post. Carbon was run at the highest thinking setting. Most rival numbers are self-reported by each provider, per Google’s evaluation methodology.

Agentic coding: Carbon scores 65.2% on Terminal-Bench 4.0 (AA), ahead of Opus 5.5 at 59.6%. It hits 9.6% on SWE-sweep, narrowly ahead of GPT-6 Astra at 9.5%. On FrontierSWE, GPT-6 Astra leads at 65.5% versus Carbon’s 61.2%.

Computer use and knowledge work: Carbon reaches 77.4% on Agents’ Last Exam v1.1. On Vending-Bench 2, it ends with a $16,484 bank balance, the highest listed.

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Long context and science: GraphWalks is the widest gap: 96.2% versus 71.8% for GPT-6 Astra. Carbon also posts 80.0% on RiemannBench.

Why does Carbon relevant for cybersecurity?

Google frames Carbon around the full defense lifecycle: find, validate, then fix. Cyber benchmarks were evaluated without guardrails.

Finding: 88.2% on SEC-bench Pro, versus 85.4% for GPT-6 Astra.
Exploiting: 54.8% on ExploitGym, versus 42.4% for GPT-6 Astra and 34.5% for Opus 5.5.
Patching: 71.0% on Collinear’s CWE-bench, versus 68.0% for Argon and GPT-6 Astra.

Google computed SEC-bench Pro and ExploitGym scores itself, using its Antigravity harness. ExploitGym ran with mitigations off. Internally, Carbon showed more than 2x higher success at generating secure code than Argon. Google team states that the Chrome team found Carbon over 10x better than another lab’s model at high-quality patches. Wiz reports Carbon leading its web penetration testing benchmarks.

What safety changes ship with Carbon?

Carbon keeps Argon’s layered stack: training, real-time misuse defenses, prompt-injection resilience and chain-of-thought monitoring. Google reports several alignment gains:

Successful cheating on Terminal-Bench 4.0 fell to 0.0%, with cheating attempts at 0.4%.
High-severity misalignment alerts in live internal coding dropped by almost 80%.
Unsupported claims fell roughly 2x versus Argon.
Overreach fell 26% and under-service fell 29%.
Attack success on Gray Swan’s Indirect Prompt Injection benchmark is 0.0%, down from Argon’s 0.7%.

These are Google’s internal or self-run figures. Independent replication is not yet available.

How can developers access Gemini 4 Carbon?

Not yet, for most. Google is participating in the U.S. government’s voluntary pre-release testing process. This week, a subset of cyber defenders gets access. More trusted partners follow through the Fairwind Program, which prioritizes governments, critical infrastructure operators and core tech platforms. Developers, businesses and consumers get Carbon later, with no date given.

Pricing matches Argon’s introductory rate: $2 per million input tokens and $10 per million output tokens. Cached input is 95% off. After the introductory period, prices rise to $4 and $20.

Gemini 4 Carbon vs competitors

Feature	Gemini 4 Carbon	Gemini 4 Argon	GPT-6 Astra	Claude Opus 5.5	Claude Fable 5.1
Parameters	Not disclosed	Not disclosed	Not disclosed	Not disclosed	Not disclosed
Context window	Not disclosed	1,048,576	1,050,000	1M	1M
Max output	Not disclosed	65,536 (1,048,576 long decode)	128,000	128K	128K
Price in / out (per 1M)	$2 / $10 intro, then $4 / $20	$2 / $10 intro (Google)	$10 / $50	$4 / $20 (pricing)	$10 / $50 (pricing)
Access	Vetted cyber defenders, Fairwind	Gemini API	OpenAI API	Claude API	Claude API
AA Index v5.0	59	50	57	58	49
SEC-bench Pro	88.2%	83.4%	85.4%	Not disclosed	Not disclosed
ExploitGym	54.8%	42.0%	42.4%	34.5%	Not disclosed
OSWorld-2.1	81.5%	74.7%	80.9%	87.2%	76.8%
FrontierSWE	61.2%	55.0%	65.5%	62.3%	56.3%

Benchmark rows: Google launch post; rival scores mostly self-reported per methodology.

Key Takeaways

Gemini 4 Carbon ranks #1 on Artificial Analysis Index v5.0 at 59.
It leads SEC-bench Pro (88.2%), ExploitGym (54.8%) and CWE-bench (71.0%).
GraphWalks long context hits 96.2%, its widest margin.
Claude Opus 5.5 still wins OSWorld-2.1; GPT-6 Astra wins FrontierSWE.
Access starts with cyber defenders at $2/$10 per million tokens.

Check out the Google’s launch post, the evaluation methodology and the Fairwind Program page. Also,feel free to follow us on**Twitterand don’t forget to join our150k+ML SubRedditand Subscribe toour Newsletter. Wait! are you on telegram?now you can join us on telegram as well.**

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Asif Razzaq

Website|+ posts Bio ⮌

Asif Razzaq is the CEO of Marktechpost AI Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.

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