Average Ratings 0 Ratings
Average Ratings 0 Ratings
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
Qwen3.5 represents a major advancement in open-weight multimodal AI models, engineered to function as a native vision-language agent system. Its flagship model, Qwen3.5-397B-A17B, leverages a hybrid architecture that fuses Gated DeltaNet linear attention with a high-sparsity mixture-of-experts framework, allowing only 17 billion parameters to activate during inference for improved speed and cost efficiency. Despite its sparse activation, the full 397-billion-parameter model achieves competitive performance across reasoning, coding, multilingual benchmarks, and complex agent evaluations. The hosted Qwen3.5-Plus version supports a one-million-token context window and includes built-in tool use for search, code interpretation, and adaptive reasoning. The model significantly expands multilingual coverage to 201 languages and dialects while improving encoding efficiency with a larger vocabulary. Native multimodal training enables strong performance in image understanding, video processing, document analysis, and spatial reasoning tasks. Its infrastructure includes FP8 precision pipelines and heterogeneous parallelism to boost throughput and reduce memory consumption. Reinforcement learning at scale enhances multi-step planning and general agent behavior across text and multimodal environments. Overall, Qwen3.5 positions itself as a high-efficiency foundation for autonomous digital agents capable of reasoning, searching, coding, and interacting with complex environments.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
APIFree
No
Alibaba Cloud Model Studio
No
BaseRT
No
Claw Code
No
Google Cloud Platform
Yes
Meta AI
Yes
Microsoft-Decision-1
No
Muse
Yes
Muse Code
Yes
Ollama
No
Integrations
APIFree
Yes
Alibaba Cloud Model Studio
Yes
BaseRT
Yes
Claw Code
Yes
Google Cloud Platform
No
Meta AI
No
Microsoft-Decision-1
Yes
Muse
No
Muse Code
No
Ollama
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
Free
Open source
Free Trial
No
Free Version
Yes
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
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
Yes
Mac
Yes
Linux
Yes
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
No
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
Alibaba
Founded
1999
Country
China
Website
qwen.ai