Gemini 4 Carbon
A frontier large language model from Google DeepMind built for long-horizon coding and agentic work, with a focus on cybersecurity tasks such as finding, exploiting and patching vulnerabi...
A frontier large language model from Google DeepMind built for long-horizon coding and agentic work, with a focus on cybersecurity tasks such as finding, exploiting and patching vulnerabi...
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Gemini 4 Carbon is a practical fit for buyers evaluating Artificial Intelligence and Tech tools.
- What is Gemini 4 Carbon?
- A frontier large language model from Google DeepMind built for long-horizon coding and agentic work, with a focus on cybersecurity tasks such as finding, exploiting and patching vulnerabilities.
- Who is it for?
- Cyber defenders and security engineering teams at organizations admitted to Google's early access, followed later by developers and businesses building long-running coding agents once broader access opens.
- Pricing
- unknown
Product information
What this product does
Gemini 4 Carbon is our strongest model with state-of-the-art cybersecurity and long horizon capabilities.
Sourced from blog.google
Works With
Platforms
- Web
- iOS
AI Models
- Gemini 4 Carbon
- Gemini 4 Argon
Company & Maker
Who built Gemini 4 Carbon?
- Company
- Product
- A frontier large language model from Google DeepMind built for long-horizon coding and agentic work, with a focus on cybersecurity tasks such as finding, exploiting and patching vulnerabilities.
- HQ
- Mountain View
- Founded
- 1998
- Team size
- 5000+
- Live since
- Oct 2026
- Founders
- Larry Page - Co-founder
- Sergey Brin - Co-founder
Some facts above are based on publicly available information and may have changed.
“Our researchers and models are working hand in hand to advance capabilities faster, while strengthening our safeguards in tandem.”
Problem Solved
What problem does Gemini 4 Carbon solve?
It's for security teams and engineers who chase subtle bugs and vulnerabilities across large codebases and low-level systems. That work takes many steps, and models that stall at a dead end leave people to restart and supervise each attempt. Google says Carbon can keep working on such tasks for days, and it reports 88.2% on SEC-bench Pro for finding vulnerabilities and 71.0% on CWE-bench for patching. Example: a cyber defense team in the early access group asks it to root-cause a memory bug across a large codebase and propose a patch.
Researched with AI · Data refreshed yesterday · How we score
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Who should use
Buyer-fit is derived from approved personas and use-case evidence. Search-volume noise stays labeled separately.
Best-fit workflows
- Find and patch vulnerabilities across a large codebase
- Root-cause subtle bugs in long-running agent sessions
- Build full-stack web apps from prompts
Pricing reality
Last updated Oct 2026
Starting price
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LIVE ESTIMATEEstimated spend
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Activity signals
Insufficient data
Only 6 of 5 minimum public signals linked. Score appears once we collect 0 more independent signals.
HOW THIS TOOL SCORES
Scores compare this tool with 49 similar tools in Artificial Intelligence. Use it to judge Innovation and Capability before you shortlist.
Innovation Index™
Is it new, different, and easy to understand?
Medium confidence (60%) - some sub-scores have thin evidence.What this means →
Functionality Score™
Does it cover the jobs buyers expect, and can it keep up?
Low confidence (30%) - some sub-scores have thin evidence.What this means →
Data sources
Traffic & search proof
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Google Trends live interest
Live Google Trends data for Gemini 4 Carbon: interest over time, regions, topics, and related queries.
Tracks 12-month Google search interest on a 0-100 relative interest scale.
Includes regional demand, so readers can see where search interest is strongest.
Includes related topics and related queries to surface adjacent demand and search intent.
FAQ
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Community & reach
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Social mentions
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17 in the last 30 days
Showing top 3 of 17 linked mentions.
Sources and methodology
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