Muse Ember by Meta

Product Introduction

1. Definition: Muse Ember by Meta is a lightweight multimodal reasoning model engineered specifically for high-volume agentic workflow execution, coding subtasks, document extraction, and tool routing. Categorized as a small language model (SLM) with multimodal capabilities, it processes text, images, audio, and video inputs within a 1-million-token context window.
2. Core Value Proposition: Muse Ember exists to offload the repetitive, high-volume computational labor traditionally assigned to larger, more expensive frontier models. By combining high-speed execution—reaching p95 latencies as low as 2.2 seconds on coding tasks—with enterprise-grade context lengths and cost-effective pricing starting at $0.10 per million input tokens, Ember enables engineering teams and AI developers to scale automated agent architectures economically.

Main Features

1. High-Volume Agentic Execution & Routing: Designed to act as a subagent within complex multi-agent frameworks, Muse Ember handles repetitive tasks such as tool calls, API request formatting, file routing, and subtask decomposition. It works synergistically with larger planning models (such as Muse Wonder or Muse Spark), managing the heavy lifting of execution while the flagship models handle strategic reasoning and orchestration.
2. Native Multimodal Scalability: The model processes disparate data types concurrently—including text, high-resolution images, audio transcripts, and video streams—within a unified 1M-token context window. This capability allows autonomous agents to perform complex document analysis (e.g., contract auditing against negotiation playbooks) and cross-modal data extraction at scale without performance degradation.
3. Optimized Token Efficiency & Cost-Effective Pricing: Muse Ember achieves up to 36% to 48% fewer output tokens compared to comparable open-weight and proprietary models on benchmark tasks. Its tiered pricing model charges $0.10 per million input tokens ($0.004 cached) for contexts under 200k tokens, and $0.30 per million input tokens ($0.012 cached) for contexts exceeding 200k tokens, with output tokens priced at $0.50 to $1.50 per million.

Problems Solved

1. Pain Point: High operational costs and high latency associated with routing all agentic workflows, document extractions, and coding subtasks through massive, general-purpose frontier LLMs.
2. Target Audience: AI Engineers, Full-Stack Developers, Enterprise Automation Architects, Machine Learning Practitioners, and DevOps Teams building autonomous coding bots, multi-agent systems, and document processing pipelines.
3. Use Cases: Automated terminal coding and repository investigation (via integrations like Muse Code or OpenCode), clause-by-clause contract risk auditing, candidate resume screening against hiring briefs, multi-agent workflow fan-out into isolated git worktrees, and real-time search-grounded enterprise data retrieval.

Unique Advantages

1. Strengths & Limitations (Pros & Cons):
   • Strengths: Exceptional speed (up to 30% faster tail-latency than competitor models), massive 1M-token context window, superior token efficiency (reducing output bloat by up to 48%), multimodal support across text, image, audio, and video, and ultra-low cost ($0.10/M input tokens).
   • Limitations: As a lightweight reasoning model, it is not optimized for high-level zero-shot architectural planning or complex multi-step strategic problem-solving on its own; it requires pairing with larger orchestrator models (like Muse Wonder) for end-to-end complex tasks.
2. Key Alternatives & Differentiation: Compared to Claude Haiku and GPT-6 Luna, Muse Ember outperforms them on specific agentic automation benchmarks such as AutomationBench-AA (37.8 vs. 13.9 and 20.2) and Terminal-Bench 4.0, while delivering significantly lower latency (p95 of 2.2 seconds). Unlike models constrained by smaller context limits, Ember provides a full 1M-token window with competitive prompt caching economics ($0.004-$0.012 per million cached tokens) and seamless integration through the Meta Model API, Google Cloud Marketplace, and OpenRouter.

Frequently Asked Questions (FAQ)

1. What is Muse Ember by Meta? Muse Ember is Meta's smallest multimodal reasoning model designed specifically for high-volume agentic workflow execution, coding subtasks, document extraction, and tool routing. It supports text, images, audio, and video with a 1-million-token context window.
2. How much does Muse Ember cost to use via the API? Pricing starts at $0.10 per million input tokens and $0.50 per million output tokens for contexts under 200k tokens. For contexts exceeding 200k tokens, input tokens cost $0.30 per million and output tokens cost $1.50 per million, with cached input pricing dropping as low as $0.004 per million tokens.
3. What makes Muse Ember ideal for AI agents and subagent workflows? Muse Ember is purpose-built to handle repetitive subagent tasks like API tool calls, multi-repo investigations, document summarization, and coding subtasks. It delivers significantly lower latency (p95 of 2.2 seconds) and higher token efficiency than traditional frontier models, making it cost-effective for running parallel subagents in an agent harness.
4. Where can developers access and deploy Muse Ember? Developers can access Muse Ember through the Meta Model API, OpenRouter, Google Cloud Marketplace, and Meta's developer terminal tool, Muse Code.