Inkling-Small

Inkling-Small

Thinking Machines Lab
SmolLM2

SmolLM2

Hugging Face
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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

SmolLM2 is a collection of state-of-the-art, compact language models developed for on-device applications. The models in this collection range from 1.7B parameters to smaller 360M and 135M versions, designed to perform efficiently even on less powerful hardware. These models excel in text generation tasks and are optimized for real-time, low-latency applications, providing high-quality results across various use cases, including content creation, coding assistance, and natural language processing. SmolLM2's flexibility makes it a suitable choice for developers looking to integrate powerful AI into mobile devices, edge computing, and other resource-constrained environments.

Platforms Supported

Windows Not Supported
Mac Not Supported
Linux Not Supported
Cloud Supported
On-Premises Not Supported
iPhone Not Supported
iPad Not Supported
Android Not Supported
Chromebook Not Supported

Platforms Supported

Windows Supported
Mac Supported
Linux Supported
Cloud Not Supported
On-Premises Supported
iPhone Supported
iPad Supported
Android Supported
Chromebook Not Supported

Audience

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

Audience

Developers and AI enthusiasts looking to integrate compact, high-performance language models into mobile apps, edge computing systems, or other devices with limited processing power

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Not Supported

API

Offers API Supported

API

Offers API Not Supported

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 Not Supported
Free Trial Not Supported

Pricing

Free
Open source
Free Version Supported
Free Trial Not Supported

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

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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.

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported

Company Information

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

Company Information

Hugging Face
Founded: 2016
United States
huggingface.co/collections/HuggingFaceTB/smollm2-6723884218bcda64b34d7db9

Alternatives

Alternatives

BitNet

BitNet

Microsoft
Gemini Nano

Gemini Nano

Google
Inkling

Inkling

Thinking Machines Lab
Beam

Beam

Reflection

Categories

Categories

AI Coding Models Supported
AI Models Supported

Integrations

Hugging Face Not Supported
Locally AI Not Supported
Mirai Not Supported
Model Context Protocol (MCP) Supported
Private Mind Not Supported
Runpod Not Supported
Tinker Supported

Integrations

Hugging Face Supported
Locally AI Supported
Mirai Supported
Model Context Protocol (MCP) Not Supported
Private Mind Supported
Runpod Supported
Tinker Not Supported
Claim Inkling-Small and update features and information
Claim Inkling-Small and update features and information
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