Meta launches Muse Ember: A small AI model with powerful capabilities

Meta has expanded its intelligence portfolio with the launch of Muse Ember, a small AI model designed to handle repetitive tasks that use a lot of resources at a lower cost. Meta CEO Mark Zuckerberg believes the new model delivers performance despite its smaller size positioning it as a strong competitor, in its category.

Announcing the launch, Zuckerberg highlighted the model's efficiency and capabilities in a post on X.

“Releasing Muse Ember today - our smallest model that delivers higher intelligence per dollar than others in its class,” Mark Zuckerberg wrote on X.

He added that Ember was developed for “token-heavy work” carried out by AI agents throughout the day. Its intended applications include “coding subtasks, routing, extraction, and long document parsing.”

What makes Muse Ember different?

Unlike large, advanced AI systems designed to handle complex reasoning and planning, Muse Ember focuses on completing repetitive tasks efficiently. Meta intends it to work alongside larger models, including Muse Wonder, rather than operate as a replacement.

The company explained this approach with the statement, “Muse Wonder plans, Muse Ember handles the volume.”

For instance, in a customer-support environment, Muse Wonder could develop a strategy for resolving customer complaints, while Ember handles the repetitive process of applying those instructions across thousands of requests.

According to Alexandr Wang, who leads Meta's Superintelligence Labs, Muse Ember is the company's first released model trained using Muse Wonder Full.

The model also supports types of content including text, images, audio and video. This allows the model to analyse screenshots, charts, scanned documents, voice recordings, meeting discussions and videos without depending on AI models.

Ember comes with a context window of one million tokens and can generate up to 512,000 output tokens, making it suitable for processing large amounts of information.

Meta highlights Ember's performance and pricing

Muse Ember is positioned against competing models such as Anthropic's Claude Haiku 5.5 and OpenAI's GPT-6 Luna. According to benchmarks published by Meta, Ember performs better than both models in several tests.

On Terminal-Bench 4.0, Ember recorded a score of 40.9 per cent, compared with 39.2 per cent for Claude Haiku and 16.4 per cent for GPT-6 Luna.

Meta has priced Muse Ember at $0.10 per million input tokens and $0.50 per million output tokens.

The company also claimed that Ember matched Claude Haiku 5.5's highest Artificial Analysis Intelligence Index score at approximately one-third of its cost. Both models recorded rounded scores of 36 at their highest settings, while GPT-6 Luna scored 28.

Meta brings Muse AI to smartwatches

Separately, Meta has expanded its Muse personal AI assistant to smartwatches, including the Apple Watch and devices running Google's Wear OS.

Users can interact with Muse through voice dictation or text to manage schedules, collect information and make reservations. The smartwatch interface also features Muse's yeti-like avatar, which users can tap to start conversations.

To use the feature, you need the Muse smartphone application. I recommend keeping the Muse smartphone application updated. Apple Watch support requires watchOS 9.0 or later. The iPhone app needs iOS 18.0 or later. Android users must have Android 10 or newer. Android users can also download the Wear OS companion app from the Google Play Store.

However, Muse remains limited to select markets, including the US, Canada, Singapore and Japan. An India launch is reportedly being considered.

ABOUT THE AUTHOR

Kahekashan

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Kahekashan is a passionate technophile with a keen eye for cutting-edge gadgets, emerging technologies, and everything in the digital realm. Raised in a Defence family with strong values and a background in literature, she has consistently pursued excellence in every endeavour. Her last full-time assignment involved content writing with the Indian School of Business.

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