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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 GPT-4.1 nano?

GPT-4.1 nano is a highly efficient, smaller-scale version of the GPT-4.1 model, built for high-speed, low-cost AI applications. It retains the core capabilities of the GPT-4.1 series, including support for a 1 million token context window, but with optimized performance for tasks like classification, search, and autocompletion. Designed to be both affordable and fast, GPT-4.1 nano is perfect for developers and businesses looking for a quick, reliable AI solution that minimizes latency and operational costs.

Media

Media

Integrations Supported

Google Cloud Platform
Meta AI
Muse
Muse Code

Integrations Supported

BLACKBOX AI
Cheaper Inference
Devin Desktop
GPT-4.1
GPT-4.1 mini
GitHub Copilot
HTML
Microsoft Foundry
Microsoft Foundry Models
OpenAI
Qodo
Snowflake
Snowflake Cortex AI
T3 Chat

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

$0.10 per 1M tokens (input)
$0.10 per 1 million tokens (input)
$0.025 per 1 million tokens (cached input)
$0.40 per 1 million tokens (output)

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

OpenAI

Date Founded

2015

Company Location

United States

Company Website

openai.com/index/gpt-4-1/

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

Foundation Models

Not specified

Large Language Models

Not specified

Multimodal Models

Not specified

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