For developers building autonomous agents, the biggest bottleneck is rarely raw intelligence. It is cost. Agents do not solve one hard problem; they execute thousands of small, structured steps every day. Reading a file, calling an API, parsing a log line, and updating a database are repetitive tasks that consume tokens at scale. Until now, teams had to choose between expensive frontier models or less capable lightweight options. Meta has just changed that equation with Muse Ember.
Released on November 9, 2026, Muse Ember is designed specifically for high-volume agentic work. It targets the "commodity" layer of AI inference: tool calls, coding subtasks, routing, and long document extraction. The goal is simple. Deliver maximum utility per dollar so that sophisticated automation can run in every product, not just premium tiers.
The Efficiency Frontier
Muse Ember sits on the efficiency frontier of the Artificial Analysis leaderboard. No other model scores higher for less money per task. In direct comparisons, it matches the best score of Claude Haiku 5.5 at roughly one-third of the cost. It also outperforms every reasoning setting of GPT-6 Luna.
The pricing structure reflects this focus on volume. At $0.10 per million input tokens and $0.50 per million output tokens, the model is priced for continuous operation. For context, if you are building complex frontends that need to process user interactions rapidly, the low latency here matters as much as the backend logic. Just as React optimized how we build interfaces by batching updates, Muse Ember optimizes the cognitive workload by handling the routine steps without the overhead of a larger model.
- Cost per task: approximately $0.14
- Context window: 1M tokens
- Max output: 512K tokens
- Intelligence Index: 36 (max reasoning)
Built for Agent Workflows
Agents spend most of their tokens on many small, structured steps. Muse Ember is engineered for exactly this pattern. It can carry a business workflow end-to-end, pulling records, calling tools in sequence, and updating systems across different applications. Its 1M-token context window allows it to ingest a full codebase or a large set of documents in a single call, eliminating the need for chunking strategies that often degrade accuracy.
In early testing, partners noted significant speed improvements. Cloudflare reported that Muse Ember ran up to 30% faster at the tail compared to comparable models. You.com observed a p95 latency of 2.2 seconds, which is four to thirteen times faster than leading open-weight models. This speed makes it ideal for real-time user interactions where waiting for a response breaks the flow. If you are integrating this into a web application, consider how fast feedback loops influence user retention. Frameworks like Vue.js rely on reactive data binding to keep UIs snappy; similarly, Muse Ember keeps the agent loop tight by returning results quickly.
Coding and Multimodal Capabilities
While specialized models like Muse Spark handle deep coding challenges, Muse Ember handles the volume. It excels at terminal-based coding tasks, file reading, and running tests. On Terminal-Bench 4.0, it scored 40.9%, beating Claude Haiku 5.5 (39.2%) and significantly outperforming GPT-6 Luna (16.4%).
The model also natively understands text, images, audio, and video. This means an agent can read screenshots, analyze charts, or process voice memos without switching to a separate vision model. On OSWorld 2.1, a benchmark for computer use via desktop apps, Muse Ember achieved a 69.5% score, rivaling the top performers. This multimodal depth ensures that the model can interpret visual context from user screens or scanned documents directly.
Safety and Availability
Despite its size, Muse Ember underwent rigorous safety evaluation under Meta’s Superintelligence Scaling Framework. Tests covered chemical and biological risks, cybersecurity threats, and loss-of-control scenarios. The model remained below high-risk thresholds and demonstrated strong adherence to user intent, including resistance to hidden instructions in web pages or tool outputs.
Muse Ember is available today on the Meta Model API and Muse Code. It supports the Responses API and is accessible via Google Cloud and Oracle Cloud. For developers managing environment variables and configuration for these new endpoints, keeping your setup clean is crucial. Recent updates to Node.js have made this easier, particularly with native support for .env files, allowing you to manage API keys securely without extra dependencies.
What It Means for Developers
Muse Ember represents a shift in how we architect AI products. Instead of using a single massive model for everything, we can now use a tiered approach. Use Muse Wonder for planning and hard problems, and offload the repetitive execution to Muse Ember. This strategy reduces costs dramatically while maintaining high performance. As more efficient models come online, the barrier to entry for building intelligent, autonomous systems continues to drop. The question is no longer whether your app can use AI, but how efficiently it can run it.