Meta Superintelligence Labs has introduced Muse Ember, a new artificial intelligence model designed to handle repetitive tasks performed by AI agents. Announced on November 9, 2026, the model focuses on activities such as calling external tools, completing coding subtasks, extracting information, routing requests and processing lengthy documents. Meta describes it as its smallest and most efficient model yet, with a particular emphasis on delivering more intelligence at a lower cost per completed task.
The launch represents another step in Meta’s strategy to build a complete portfolio of AI models for different workloads. Rather than using an expensive, advanced model for every operation, Meta wants developers to combine powerful reasoning models with smaller systems that execute routine tasks at scale. Muse Ember is available through the Meta Model API and Muse Code, with pricing starting at $0.10 per million input tokens and $0.50 per million output tokens.
Key Takeaways
- Meta Superintelligence Labs launched Muse Ember on November 9, 2026.
- It is Meta’s smallest and most efficient model in its Muse portfolio.
- The model is designed for repetitive AI agent tasks, including coding, tool calls, routing and document extraction.
- API pricing starts at $0.10 per million input tokens and $0.50 per million output tokens.
- Cached input tokens cost $0.004 per million tokens.
- Muse Ember supports a context window of up to 1 million tokens and output of up to 512,000 tokens.
- Meta claims it can match Claude Haiku 5.5’s best Artificial Analysis Intelligence Index score at approximately one-third of its cost per task.
- Early partners include Box, Cline, Oracle, Cloudflare and You.com.
- The model is designed to work alongside larger models such as Muse Wonder.
What Is Meta Muse Ember?
Muse Ember is a lightweight AI model developed by Meta Superintelligence Labs, the company’s dedicated artificial intelligence research organisation.
Unlike large AI models designed to handle difficult reasoning problems, Muse Ember focuses on the smaller operations that AI agents perform repeatedly.
An AI agent typically breaks a complicated request into multiple steps. For example, a coding agent might need to inspect files, identify a problem, modify code, run tests and check the results.
A powerful model can plan the overall task, but using that same expensive model for every small operation can increase costs.
Muse Ember is designed to handle these repeated operations more efficiently.
Meta identifies several primary use cases:
- Tool calls: Interacting with external applications and software tools.
- Coding subtasks: Reading files, running tests and making targeted code changes.
- Routing: Directing requests to the appropriate tools or specialised agents.
- Information extraction: Finding and organising relevant information from large datasets.
- Document processing: Reading and analysing lengthy documents and collections of files.
The model is also multimodal, meaning it can process text, images, audio and video. This allows agents to work with screenshots, charts, scanned documents, voice recordings and meeting videos without necessarily requiring a separate model for each format.
How Muse Ember Fits Into Meta’s AI Strategy
Meta is building a portfolio of AI models rather than relying on a single system for every task.
Its Muse family includes models designed for different levels of complexity and different types of workloads.
Muse Ember is positioned as the execution-focused model in this portfolio.
| Model | Primary purpose |
|---|---|
| Muse Wonder | Complex reasoning and difficult problems |
| Muse Spark | Coding and broader agentic workflows |
| Muse Glimmer | Open-weight model for local AI agent workloads |
| Muse Ember | Repetitive coding and agent tasks at scale |
The central idea is to divide work between models.
For example, a company building an automated customer-support system could use a more capable model to establish how different customer problems should be handled. Muse Ember could then process thousands of individual requests, retrieve information, call tools and update records according to those instructions.
Meta summarises this approach with a simple division: Muse Wonder plans, while Muse Ember handles the volume.
This architecture could help businesses reduce the cost of running AI agents, particularly when applications require thousands or millions of small model interactions.
Muse Ember Pricing: $0.10 Per Million Input Tokens
Pricing is one of the most important parts of the Muse Ember launch.
Meta is positioning the model as an economical option for developers building AI agents that perform repeated operations.
The standard API pricing is:
| Pricing component | Cost |
|---|---|
| Input tokens | $0.10 per million |
| Cached input tokens | $0.004 per million |
| Output tokens | $0.50 per million |
The cached-input price is particularly relevant for applications that repeatedly send the same instructions, tool definitions or reference material to an AI model.
Instead of paying the full input price every time eligible information is reused, developers can benefit from the lower cached-input rate.
A one-million-token context window
Muse Ember supports a context window of up to 1 million tokens.
A context window determines how much information a model can consider during an interaction. A larger window can be useful when an agent needs to process extensive documentation, large software projects or multiple files.
The model can also generate up to 512,000 output tokens.
These capabilities make it suitable for applications involving long documents and complex multi-step workflows, although actual performance and costs will depend on how developers structure their requests.
Meta Claims Muse Ember Can Compete With Claude Haiku and GPT-6 Luna
Meta is positioning Muse Ember against smaller, cost-efficient models from OpenAI and Anthropic.
In its November 9 announcement, the company published benchmark comparisons using Artificial Analysis evaluations.
Meta reported that Muse Ember achieved an Intelligence Index score of 36 at its maximum reasoning setting. Claude Haiku 5.5 also scored 36, while OpenAI’s GPT-6 Luna scored 28 in the comparison published by Meta.
The company also compared estimated costs per completed task.
| Model | Intelligence Index | Estimated cost per task |
|---|---|---|
| Muse Ember | 36 | $0.14 |
| Claude Haiku 5.5 | 36 | $0.41 |
| GPT-6 Luna | 28 | $0.15 |
According to Meta’s figures, Muse Ember achieved a comparable Intelligence Index score to Claude Haiku 5.5 at roughly one-third of its estimated task cost.
However, these figures require context. The cost estimates use published model pricing and benchmark workloads. Actual costs can vary according to prompt length, output length, reasoning settings, caching and the agent framework being used.
Muse Ember does not win every benchmark
Meta’s published results show that Muse Ember performs differently depending on the task.
| Benchmark | Muse Ember | Claude Haiku 5.5 | GPT-6 Luna |
|---|---|---|---|
| AutomationBench-AA | 37.8 | 13.9 | 20.2 |
| Terminal-Bench 4.0 | 40.9 | 39.2 | 16.4 |
| Coding Agent Index-AA | 46 | 37 | 31 |
| FrontierCode 2.0-AA | 27.2 | 34.9 | 25.2 |
| OSWorld 2.1 | 69.5 | 68.9 | 50.5 |
The figures are based on Meta’s published comparisons, which combine external benchmark results and company-reported evaluations. Some tests use different model-specific execution environments.
One important exception is FrontierCode 2.0-AA, where Claude Haiku 5.5 scored higher than Muse Ember.
This suggests that Muse Ember’s appeal is not necessarily that it is the best model for every complicated programming task. Its positioning is based on delivering useful performance at a lower overall cost for repetitive workloads.
Early Partners Include Box, Oracle, Cloudflare and You.com
Meta worked with several technology companies during the early testing of Muse Ember.
The partners include Box, Cline, Oracle, Cloudflare and You.com.
Their feedback highlights potential applications in enterprise document processing, coding, software development and automated workflows.
Box
Box tested Muse Ember on enterprise content-related tasks.
Its examples included reviewing contracts against company negotiation policies and evaluating candidates against detailed recruitment requirements.
The focus is on helping organisations extract useful information from large collections of documents.
Cline
Cline highlighted Muse Ember’s potential as a lightweight model for developers using AI coding agents.
The company described it as useful for repetitive subtasks that can be delegated to smaller models within a larger agent workflow.
Oracle
Oracle reported encouraging early results when using Muse Ember for complex engineering tasks.
The company sees potential applications for engineering teams that need AI assistance with technical workflows.
Cloudflare
Cloudflare highlighted the model’s speed and its ability to handle investigations across multiple software repositories.
Its CTO reported that Muse Ember ran up to 30% faster at the tail end of latency measurements than the comparable model tested.
You.com
You.com reported that Muse Ember completed every coding quest in its internal testing with a 2.2-second 95th-percentile latency.
It also reported lower output-token consumption compared with the other models included in its tests.
These are partner-reported results from early testing, not guarantees of performance across all applications.
Muse Ember Is Available Through Meta Model API and Muse Code
Developers can access Muse Ember through two main channels.
Meta Model API: Developers can integrate the model into their own applications, AI agents and enterprise workflows. It is available under the model identifier muse-ember-1.0 and supports the Responses API.
Muse Code: Developers can use the model within Meta’s coding environment to assist with software-development tasks.
Meta has also announced availability through Google Cloud and Oracle Cloud, giving developers additional infrastructure options for accessing its models.
The availability of API access is important because developers can incorporate Muse Ember into existing applications rather than having to use it as a standalone chatbot.
The model is designed to operate as part of a larger agent system, where different models and software tools work together.
What Does Muse Ember Mean for the AI Industry?
The launch reflects an important shift in the economics of artificial intelligence.
As businesses deploy more AI agents, the cost of each individual model interaction becomes increasingly important.
A single AI agent may make hundreds of calls during a complicated workflow. If every operation uses an expensive model, the total cost can become difficult to manage.
Smaller models can potentially reduce this expense by handling routine operations while more advanced models concentrate on difficult reasoning and planning.
This approach could create demand for specialised models optimised for speed, cost and reliability rather than maximum intelligence scores alone.
Meta is attempting to make its AI infrastructure attractive to developers by offering different models for different workloads.
The strategy also puts pressure on competing AI providers to improve their pricing and efficiency, particularly in the market for coding agents, enterprise automation and software integrations.
The Bigger Picture
Muse Ember shows that Meta is increasingly focused on the economics of deploying AI at scale. The next stage of AI competition may not depend only on which company develops the most capable model. It may also depend on which provider can deliver useful intelligence at the lowest cost across millions of individual operations.
For developers and businesses, smaller execution-focused models could make complex AI agents more commercially practical. However, performance, reliability and security will remain important. Meta’s published benchmarks provide an initial picture of Muse Ember’s capabilities, but independent testing across real-world workloads will determine how competitive it becomes.
Frequently Asked Questions
1. What is Meta Muse Ember?
Muse Ember is a lightweight AI model developed by Meta Superintelligence Labs. It is designed for repetitive AI agent tasks, including coding subtasks, tool calls, information extraction and document processing.
2. When was Muse Ember released?
Meta announced Muse Ember on November 9, 2026.
3. How much does Muse Ember cost?
Its standard API pricing starts at $0.10 per million input tokens and $0.50 per million output tokens. Cached input tokens cost $0.004 per million.
4. What is Muse Ember’s context window?
Muse Ember supports a context window of up to 1 million tokens and can generate up to 512,000 output tokens.
5. Is Muse Ember better than Claude Haiku 5.5?
Meta’s published Artificial Analysis comparison shows both models scoring 36 on the Intelligence Index. Meta claims Muse Ember achieves a similar score at approximately one-third of Haiku’s estimated cost per task. However, Haiku performs better on certain individual benchmarks.
6. Can developers use Muse Ember?
Yes. Developers can access it through the Meta Model API and Muse Code. It is also available through Google Cloud and Oracle Cloud.
7. What is the difference between Muse Ember and Muse Wonder?
Muse Wonder is designed for difficult reasoning and planning problems, while Muse Ember is designed to execute repetitive tasks at scale. Meta intends the two models to work together in AI agent systems.
8. Is Muse Ember open source?
Meta has made Muse Ember available through its API and developer products. The launch announcement does not offer downloadable open model weights, so it should not be described as an open-source model.
Looking Ahead
Meta is likely to continue expanding its Muse model portfolio with specialised systems that address different parts of the AI development and deployment process. Muse Ember provides a lower-cost execution option, while larger models can handle planning and complex reasoning. If this combination delivers reliable results, it could make AI agents more affordable for businesses that need to automate large numbers of repetitive operations.
For the wider industry, the launch adds another competitor to the market for efficient AI models. OpenAI, Anthropic and other AI developers are also competing to improve the cost and performance of agent-based applications. The next important test for Muse Ember will be whether developers find its claimed efficiency advantages in their own workloads, beyond the benchmarks and early partner demonstrations published at launch.
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