Muse Spark 1.2
5.0 out of 5 starsAverage Ratings 0 Ratings
Average Ratings 1 Rating
Description
Muse Ember is a multimodal AI model developed by Meta Superintelligence Labs to provide efficient reasoning and task execution for autonomous AI agents and enterprise applications. The model is optimized for high-volume workloads involving tool calls, coding subtasks, data extraction, document analysis, and automated business processes. Its one-million-token context window allows applications to process extensive software repositories, long documents, and large collections of business records in a single request. Muse Ember accepts text, images, audio, video, and PDF inputs, enabling developers to build AI systems that understand multiple forms of information without switching between specialized models. Its agentic capabilities allow applications to retrieve records, interact with software tools, execute sequential operations, and update information across connected systems. For software development, the model supports source code inspection, automated testing, targeted code modifications, and technical problem-solving within agent-driven development environments. Meta reports benchmark scores of 37.8% on AutomationBench-AA, 40.9% on Terminal-Bench 4.0, and 27.2% on FrontierCode 2.0-AA. The model can operate as a specialized execution component alongside larger models such as Muse Wonder and Muse Spark, reducing the cost of repeated operations in multi-agent workflows. Muse Ember is accessible through Meta Model API and Muse Code, with usage-based pricing of $0.10 per million input tokens, $0.004 per million cached input tokens, and $0.50 per million output tokens.
Description
Muse Spark 1.2 is Meta’s newest coding-focused model, released alongside Muse Code as part of Meta’s AI developer platform. The model improves on Muse Spark 1.1 with stronger code generation, complex debugging, codebase understanding, and full developer workflow performance. Muse Spark 1.2 powers Muse Code, a terminal coding agent that can plan changes, write code, validate results, and coordinate persistent background subagents. The model was co-trained with Muse Code so it performs well inside the agentic coding runtime and tool environment. Its training included scaled coding compute, broader training environment diversity, rejection-sampled harness trajectories, recipe optimizations, and Muse Code toolset integration. Muse Spark 1.2 is designed for long-horizon coding tasks such as whole-repository generation, large end-to-end projects, auto-research, and extended optimization work. It uses planning to sequence work, goal conditioning to stay aligned with the user’s objective, and context compaction to preserve useful knowledge over long sessions. The model also benefits from a self-improvement loop where Muse Spark 1.1 generated challenging coding environments and instruction-following templates for training. By combining coding specialization, agentic workflow support, long-horizon training, subagent compatibility, and Meta Model API availability, Muse Spark 1.2 helps developers build, debug, and optimize software more effectively.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Meta AI
Yes
Muse Code
Yes
.NET
No
Bash
No
C
No
C++
No
Cheaper Inference
No
Claude Code
No
Dart
No
Gray Swan
No
Integrations
Meta AI
Yes
Muse Code
Yes
.NET
Yes
Bash
Yes
C
Yes
C++
Yes
Cheaper Inference
Yes
Claude Code
Yes
Dart
Yes
Gray Swan
Yes
Pricing Details
$0.10 per 1M tokens (input)
$0.10 per million input tokens and $0.50 per million output tokens
Free Trial
No
Free Version
No
Pricing Details
$1.25 per 1M tokens (input)
$1.25 per million tokens in input, and $4.25 per million tokens of output
Free Trial
Yes
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
research.meta.ai/blog/muse-ember
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
meta.ai