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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 Abliterated Large V2?

Abliterated Large V2 is an advanced text-based reasoning model developed by Abliteration AI for cybersecurity analysis, autonomous AI agents, software development, and adversarial model evaluation. The model is based on Z.ai's GLM 5.3 architecture and has undergone weight-level modifications intended to reduce refusal behavior. This modification process, known as abliteration, changes internal model representations associated with rejecting certain requests rather than relying on prompt-based jailbreak techniques. Its one-million-token context window enables the processing of large software repositories, extensive technical documentation, security research materials, and long-running agent workflows. The model supports advanced code generation, debugging, technical reasoning, and multistep programming tasks requiring interaction with external tools. Cybersecurity applications include vulnerability analysis, authorized penetration testing, exploit research, security validation, and automated assessment of software weaknesses. AI safety researchers can use the model to evaluate agent behavior, test security boundaries, and examine how systems respond to adversarial scenarios. Native tool calling enables the model to interact with supported external applications, development environments, and automated testing infrastructure. Structured output functionality allows developers to generate machine-readable responses for integration with security tools and programmatic workflows. Abliterated Large V2 is hosted in FP8 precision and provides access through OpenAI-compatible chat completion and response endpoints, as well as an Anthropic-style messages interface. The model is available through Abliteration AI and third-party inference providers, with automatic prompt caching, configurable reasoning capabilities, and default zero-retention handling of API inputs and outputs.

Media

Media

Integrations Supported

Agent Search on Gemini Enterprise Agent Platform
Gemini
Gemini 3.8 Live
Gemini Enterprise
Gemini Enterprise Agent Platform
Google
Google AI Overviews
Google AI Plus
Google AI Ultra
Google Antigravity
HTML
Java
Kotlin
Lua
Model Context Protocol (MCP)
OpenClaw
Replit
SQL
Scala

Integrations Supported

Venice.ai

API Availability

Has API

API Availability

Has API

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

$3 per 1M tokens (input)
$3.00 per 1 million input tokens, $5.00 per 1 million output tokens, and $0.30 per 1 million cached input tokens

Supported Platforms

SaaS

Supported Platforms

SaaS

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

Abliteration.ai

Date Founded

2025

Company Location

United States

Company Website

abliteration.ai/

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

Large Language Models

Not specified

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