Meta Muse Ember AI Model has entered the artificial intelligence market with a focus on delivering powerful performance at a lower operating cost.
Meta announced the new model on November 9, 2026, positioning it as an efficient option for developers building AI agents and handling repetitive tasks.
Developed by Meta Superintelligence Labs, Muse Ember is the company’s smallest and most efficient model in its Muse AI family.
It is designed to handle tasks such as coding assistance, tool calls, information extraction, document processing and workflow automation.
The launch puts Meta in competition with models such as OpenAI’s GPT-6 Luna and Anthropic’s Claude Haiku 5.5.
Meta claims that Muse Ember can match the intelligence score of Claude Haiku 5.5 while completing comparable tasks at approximately one-third of the cost.
Meta Muse Ember AI Model Focuses on Lower Costs
Unlike AI models primarily designed for direct conversations, Muse Ember is built to perform repeated tasks within automated workflows.
AI agents often need to complete several smaller operations, including searching information, extracting data, calling external tools and making changes across applications. These activities can involve large numbers of model requests, making operating costs an important consideration.
Meta says Muse Ember is designed to handle this type of work efficiently. Developers can use it for coding subtasks, document analysis, routing, data extraction and other repetitive operations.
The model also supports a context window of up to one million tokens, allowing it to process large amounts of information in a single interaction. Its maximum output capacity is 512,000 tokens.
Muse Ember Pricing Compared With GPT-6 and Claude
One of the biggest highlights of the new model is its pricing structure.
Meta has announced an API price of $0.10 per million input tokens and $0.50 per million output tokens for prompts within the standard pricing tier. Cached input tokens cost $0.004 per million.
Meta says the model delivers a better intelligence-to-cost ratio than Claude Haiku 5.5 and GPT-6 Luna.
According to its comparison using Artificial Analysis evaluations, Muse Ember achieves an intelligence score of 36, matching Claude Haiku 5.5 and outperforming GPT-6 Luna, which scores 28.
The estimated cost per task is approximately $0.14 for Muse Ember, compared with $0.41 for Claude Haiku 5.5. This means developers could potentially reduce expenses when running large numbers of similar automated tasks.
However, the comparison is based on benchmark evaluations and estimated costs. Actual expenses can vary depending on the workload, reasoning settings and number of tokens processed.
How Muse Ember Performs Against Competing AI Models
Meta has published benchmark comparisons involving Muse Ember, Claude Haiku 5.5 and GPT-6 Luna across coding, automation, document processing and other AI tasks.
In the AutomationBench-AA evaluation, which measures business workflow automation, Muse Ember scored 37.8. Claude Haiku 5.5 scored 13.9, while GPT-6 Luna achieved 20.2.
In the Agents’ Last Exam evaluation, Muse Ember scored 61.6, compared with 57.0 for Claude Haiku 5.5 and 57.1 for GPT-6 Luna.
However, Muse Ember does not lead in every category. In the FrontierCode 2.0 evaluation, which examines the quality of software engineering work, Claude Haiku 5.5 scored 34.9, ahead of Muse Ember’s 27.2.
These results suggest that Meta’s model is particularly competitive in automated workflows and several coding tasks, although performance depends on the type of work being evaluated.
Where Can Developers Use Meta Muse Ember?
Meta has made Muse Ember available through its Meta Model API and Muse Code. Developers can access the model using its API identifier, muse-ember-1.0.
The API supports the Responses API and is compatible with OpenAI SDK-based clients, making integration easier for developers already working with supported tools.
The model also supports text, image, audio and video inputs, according to Meta. This multimodal capability can help AI agents process different types of information as part of automated workflows.
There is an important pricing detail for developers working with very large documents.
When the context exceeds 200,000 tokens, the pricing increases to $0.30 per million input tokens and $1.50 per million output tokens.
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For everyday users, Muse Ember is not currently positioned as a general-purpose chatbot inside Meta AI. Its primary availability is through developer-focused products.
The launch reflects Meta’s broader strategy of offering different AI models for different workloads. While its larger models handle complex reasoning and planning, Muse Ember is intended to manage repeated operations at scale.
For businesses and developers using AI agents extensively, lower processing costs could make automation more affordable.
However, real-world performance, reliability and integration requirements will determine how widely the new model is adopted.
