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What is Muse Ember?

Muse Ember is an efficient multimodal artificial intelligence model developed by Meta Superintelligence Labs for autonomous agent execution, coding assistance, complex reasoning, and large-scale business automation. The model is designed to handle the frequent, structured operations performed by AI agents, including tool calls, information retrieval, workflow routing, document extraction, and software development subtasks. It supports a context window of one million tokens and can generate up to 512,000 output tokens, allowing applications to process large repositories, extensive documents, and complex task histories. Muse Ember natively processes text, images, audio, video, and PDF content, enabling multimodal analysis without requiring separate models for each supported input type. Its visual understanding capabilities allow agents to interpret screenshots, charts, scanned documents, and interface elements while supporting computer-based automation workflows. Software engineering capabilities include reading source files, running tests, analyzing repositories, implementing targeted fixes, and completing programming tasks within autonomous coding environments. The model also supports enterprise automation workflows that involve retrieving information, evaluating records, executing tools in sequence, and updating data across connected business applications. Meta trained Muse Ember using Muse Wonder Full, creating a specialized model intended to combine strong reasoning capabilities with lower inference costs for frequently repeated tasks. Published evaluation results include 37.8% on AutomationBench-AA, 61.6% on Agents' Last Exam, 40.9% on Terminal-Bench 4.0, and 80.6% on PerceptionBench with tools. Muse Ember can be combined with larger models such as Muse Wonder and Muse Spark, allowing advanced models to handle planning and complex decisions while Ember performs high-volume execution tasks.

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.

Media

Media

Integrations Supported

Google Cloud Platform

Integrations Supported

C++
Cursor
Dart
Devin Desktop
Factory Droid
Gemini 3.5 Flash
Gemini 3.8 Flash Cyber
Gemini Enterprise Agent Platform Notebooks
Google AI Mode
Google AI Overviews
Google AI Ultra
Kubernetes
Model Context Protocol (MCP)
PHP
PowerShell
SQL
Solidity
Vercel AI Gateway
Wikis.ai

API Availability

Has API

API Availability

Has API

Pricing Information

$0.10 per 1M tokens (input)
$0.10 per million input tokens and $0.50 per million output tokens

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.

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Supported Platforms

SaaS

Customer Service / Support

Not specified

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

research.meta.ai/blog/muse-ember

Company Facts

Organization Name

Google

Date Founded

1998

Company Location

United States

Company Website

gemini.com

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

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

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