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What is Muse Spark 1.3?

Muse Spark 1.3 showcases a sophisticated AI model that significantly enhances its abilities for both agentic and programming tasks, thereby increasing its intelligence and practical utility for daily use. It is particularly adept at sustaining concentration on lengthy projects through active user collaboration, all while skillfully orchestrating multiple workflows within a cohesive thread. When confronted with an open-ended objective, the model efficiently harnesses tools to derive context from chaotic or conflicting data, addresses strategy gaps, monitors its learning trajectory, and ultimately produces a polished final outcome. In instances where prompts are vague, it takes the initiative to request clarification, seeks help when obstacles arise, and verifies its next steps before making critical decisions. The model exhibits exceptional dependability in adhering to complex, lengthy instructions, ensuring that intricate requirements are consistently honored throughout multifaceted tasks without overlooking essential constraints or deviating from the intended workflow. Furthermore, its advanced multitasking abilities allow it to effectively match incoming requests to the relevant tasks, even when users make interjections or alter the focus of prior inquiries, resulting in a fluid user experience. Consequently, Muse Spark 1.3 stands out as a highly adaptable tool suitable for diverse applications, making it a valuable asset across various fields.

What is Inkling-Small?

Inkling-Small is an efficient multimodal AI model built to deliver strong reasoning and coding performance at a fraction of Inkling’s size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters. The model was trained on NVIDIA GB300 NVL72 systems and is designed to combine high capability with more efficient inference. Inkling-Small supports native reasoning across text, images, and audio, allowing it to work across multimodal tasks without relying on separate encoders. Its context window supports up to one million tokens, making it useful for long-form reasoning, large-scale code understanding, document analysis, and agentic workflows. Users can adjust reasoning effort from minimal to extra high depending on whether they need faster responses or deeper computation. The model’s training process includes improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning. These techniques helped Inkling-Small outperform its larger counterpart on reasoning and coding benchmarks. The model performs well in coding and tool-use harnesses and exceeds 80% on SWE-bench Verified. Its encoder-free architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens. By combining efficient MoE design, one-million-token context, adjustable reasoning effort, multimodal processing, coding strength, and tool-use performance, Inkling-Small is designed for developers and teams that need capable AI with lower active compute requirements.

Media

Media

Integrations Supported

Model Context Protocol (MCP)
C
C#
Cheaper Inference
Claude Code
Codex CLI
Continue
Dart
Facebook
Go
HTML
Kubernetes
Lua
Meta Model API
Muse Code
Odysseus
OpenAI Codex
Python
R
Tinker

Integrations Supported

Model Context Protocol (MCP)
C
C#
Cheaper Inference
Claude Code
Codex CLI
Continue
Dart
Facebook
Go
HTML
Kubernetes
Lua
Meta Model API
Muse Code
Odysseus
OpenAI Codex
Python
R
Tinker

API Availability

Has API

API Availability

Has API

Pricing Information

$1.25 per 1M tokens (input)
Input: $1.25 per 1M tokens
Output: $4.25 per 1M tokens
Free Version
Free Trial Offered?

Pricing Information

$0.30 per million input tokens
$0.30 per million input tokens and $1.20 per million output tokens
Free Version
Free Trial Offered?

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Supported Platforms

SaaS
Android
iPhone
iPad
Windows
Mac
On-Prem
Chromebook
Linux

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Customer Service / Support

Standard Support
24 Hour Support
Web-Based Support

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Training Options

Documentation Hub
Webinars
Online Training
On-Site Training

Company Facts

Organization Name

Meta

Date Founded

2004

Company Location

United States

Company Website

meta.ai/

Company Facts

Organization Name

Thinking Machines Lab

Date Founded

2025

Company Location

United States

Company Website

thinkingmachines.ai/news/inkling-small/

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