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What is Gemini 4 Carbon?

Gemini 4 Carbon is an advanced multimodal artificial intelligence model developed by Google DeepMind for cybersecurity research, software engineering, long-horizon autonomous agents, and complex analytical workflows. The model builds on Gemini 4 Argon with improvements in reasoning, coding, scientific tasks, multimodal understanding, and sustained execution of multistep engineering projects. Its cybersecurity capabilities cover the vulnerability management lifecycle, including identifying software weaknesses, verifying exploitability, and generating patches that address underlying security flaws. In Google's published evaluations, Gemini 4 Carbon achieved 88.2% on SEC-bench Pro for vulnerability discovery, 54.8% on ExploitGym for exploit validation, and 71.0% on CWE-bench for vulnerability remediation. Internal evaluations also showed more than twice the secure code generation success rate of Gemini 4 Argon. The model supports long-running agentic workflows that can investigate difficult bugs, analyze large software repositories, and perform iterative problem-solving across extended execution periods. Its software development capabilities include code generation, debugging, low-level systems analysis, full-stack web development, and automated implementation of complex engineering tasks. Gemini 4 Carbon incorporates a multilayered safety architecture with misuse prevention, prompt injection defenses, behavioral monitoring, and controls intended to reduce unauthorized actions. Google reports improvements in factual reliability, task completion, and instruction alignment, including approximately half as many unsupported claims compared with Gemini 4 Argon in internal evaluations.

What is Gemini 2.0 Flash Thinking?

Gemini 2.0 Flash Thinking represents a groundbreaking AI model developed by Google DeepMind, designed to enhance reasoning capabilities by clearly expressing its thought processes. This transparency allows the model to tackle complex problems more effectively while providing users with accessible insights into how decisions are made. By unveiling its internal thought mechanisms, Gemini 2.0 Flash Thinking not only improves its performance but also increases explainability, making it an invaluable tool for applications that require a strong understanding and trust in AI solutions. Moreover, this method encourages a stronger connection between users and the technology, as it clarifies the intricacies of AI, ultimately leading to a more informed user experience. This open dialogue about its workings can also pave the way for more ethical AI practices and better user engagement.

Media

Media

Integrations Supported

Gemini
Gemini Enterprise
Gemini platform
Google AI Studio
Antigravity CLI
Bash
C#
C++
Cursor
Google AI Plus
Hermes Agent
Lua
Objective-C
PowerShell
R
Replit
Solidity
Swift
Vercel AI Gateway

Integrations Supported

Gemini
Gemini Enterprise
Gemini platform
Google AI Studio
SSSModel

API Availability

Has API

API Availability

Pricing Information

$2 per 1M tokens (input)
$2 per million input tokens and $10 per million output tokens, with cached input tokens priced at 95% off input token price.

Pricing Information

Pricing not provided

Supported Platforms

SaaS

Supported Platforms

SaaS
Android
iPhone
iPad

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

gemini.com

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

deepmind.google/technologies/gemini/flash-thinking/

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

AI Vision Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

Categories and Features

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

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