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Description
Inkling 2 Small is a general-purpose multimodal AI model developed by Thinking Machines Lab for software engineering, advanced reasoning, visual analysis, and autonomous agent applications. The model features a sparse mixture-of-experts architecture containing 594 billion total parameters, with approximately 17 billion parameters activated during inference. Its one-million-token context window supports processing large documents, software repositories, extended conversations, and complex collections of information. Inkling 2 Small accepts text and image inputs and generates text outputs, enabling visual reasoning, programming assistance, document analysis, and multimodal problem-solving. Its agentic coding capabilities support autonomous software development, code debugging, repository modifications, application creation, and verification of completed engineering tasks. The model can also perform extended research workflows involving machine learning experimentation, GPU kernel optimization, data analysis, and iterative scientific problem-solving. Configurable reasoning effort allows users to balance response speed and computational resources against the complexity of individual tasks. Thinking Machines Lab reports benchmark scores of 25.4% on FrontierCode 2.0, 72.1% on DeepSWE 1.1, and 86.3% on SWE-Bench Pro v2 Hard. Inkling 2 Small is available for inference and fine-tuning through Tinker, with model weights scheduled for release under the Apache 2.0 license after additional safety evaluations.
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.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
Google Cloud Platform
No
Meta AI
No
Model Context Protocol (MCP)
Yes
Muse
No
Muse Code
No
Tinker
Yes
Integrations
Google Cloud Platform
Yes
Meta AI
Yes
Model Context Protocol (MCP)
No
Muse
Yes
Muse Code
Yes
Tinker
No
Pricing Details
$0.30 per million input tokens
$0.30 per million input tokens, $0.03 per million cached input tokens, and $1.00 per million output tokens
Free Trial
No
Free Version
No
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
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
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
Yes
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
Thinking Machines Lab
Founded
2025
Country
United States
Website
thinkingmachines.ai/
Vendor Details
Company Name
Meta
Founded
2004
Country
United States
Website
research.meta.ai/blog/muse-ember