Inkling-Small

Inkling-Small

Thinking Machines Lab
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About

Inkling-Small is an efficient model that offers performance comparable to Inkling at a quarter of its size. It is a Mixture-of-Experts transformer with 276 billion total parameters and 12 billion active parameters, trained on NVIDIA GB300 NVL72 systems. It supports native reasoning across text, images, and audio, variable thinking effort, and context windows of up to one million tokens. Users adjust reasoning effort from minimal to extra high to balance performance and compute. Improved pre-training data, post-training with on-policy distillation from Inkling, and extended agentic coding reinforcement learning helped Inkling-Small surpass its larger counterpart on reasoning and coding benchmarks. It performs well in coding and tool-use harnesses, exceeds 80% on SWE-bench Verified, and combines strong reasoning with efficient output. Its encoder-free multimodal architecture processes audio as dMel spectrograms and images as 40-by-40-pixel patches alongside text tokens.

About

Muse Spark 1.3 is an AI model with improved performance across agentic and coding tasks, designed to be smarter and more practical for real-world work. It sustains longer-horizon tasks by collaborating with users and managing multiple workflows in a single, long thread. Given an open-ended objective, it uses tools to build context across messy or conflicting sources, correct gaps in its plan, track what it has learned, and produce a final deliverable. It asks clarifying questions when prompts are ambiguous, requests help when stuck, and confirms before taking consequential actions. The model follows complex, long-form instructions more reliably, preserving detailed requirements across multi-step tasks without dropping constraints or drifting from the requested workflow. Improved multitasking allows it to map incoming prompts to the correct task even when users interrupt or redirect previous requests.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

AI researchers and developers seeking an efficient open-weight multimodal model for reasoning, agentic tasks, and lower-cost deployment

Audience

Developers and professional users seeking an AI model for complex agentic workflows, long-running tasks, coding, and multi-step work requiring reliable instruction following

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

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

Pricing

$1.25 per 1M tokens (input)
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 5.0 / 5

Pros & Cons from Real Users

Pros

  • Inkling-Small is really interesting from a developer’s point of view because it hits a sweet spot between serious model capability and practical deployability. A 276B-parameter model with only 12B active parameters per token is exactly the kind of architecture that makes sense if you care about cost, speed, and scaling real AI workflows. I also like that it is open weights under Apache 2.0. That makes it way more appealing for developers who want to fine-tune, inspect, customize, or build on top of the model without being completely locked into a closed API. The multimodal support is a big plus too. Being able to work with text, images, and audio inputs gives Inkling-Small a lot of room for developer tools, coding agents, support bots, document workflows, and internal automation.

Cons

  • The main downside is that “small” here is still not tiny. Even with only 12B active parameters, this is still a large open model that will require real infrastructure if you want to host it yourself. I would also want to test it deeply before making it the backbone of a production coding agent. The model card and early coverage look promising, but real developer workflows expose problems that benchmarks do not always catch: messy repos, flaky tests, weird dependencies, tool failures, and long multi-step tasks.

Pros & Cons from Real Users

Pros

  • This feels like the version where Muse Spark becomes much more interesting for developers. The focus is not just “better chat,” but better coding, better agentic workflows, and more reliable long-running tasks. The biggest win is efficiency. Meta says Muse Spark 1.3 uses fewer tokens and fewer tool calls on coding tasks, which matters a lot if you are running agents repeatedly instead of asking one-off questions. I also like that it is available in both Muse Code and the Meta Model API. That gives developers a practical path whether they want a coding-agent experience or want to plug the model into their own workflows.

Cons

  • Have not found any downsides thus far. This model is a big leap for Meta

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Thinking Machines Lab
Founded: 2025
United States
thinkingmachines.ai/news/inkling-small/

Company Information

Meta
Founded: 2004
United States
meta.ai/

Alternatives

Alternatives

GPT-5.6 Sol

GPT-5.6 Sol

OpenAI
Inkling

Inkling

Thinking Machines Lab

Categories

Categories

Integrations

Model Context Protocol (MCP)
.NET
C++
Codex CLI
Continue
Facebook
Facebook Messenger
HTML
Hermes Agent
Instagram
Java
JavaScript
Kubernetes
LlamaIndex
Meta AI
Muse Code
Odysseus
OpenAI Codex
Scala
Solidity

Integrations

Model Context Protocol (MCP)
.NET
C++
Codex CLI
Continue
Facebook
Facebook Messenger
HTML
Hermes Agent
Instagram
Java
JavaScript
Kubernetes
LlamaIndex
Meta AI
Muse Code
Odysseus
OpenAI Codex
Scala
Solidity
Claim Inkling-Small and update features and information
Claim Inkling-Small and update features and information
Claim Muse Spark 1.3 and update features and information
Claim Muse Spark 1.3 and update features and information