Inkling-Small

Inkling-Small

Thinking Machines Lab
SubQ 1.1 Small

SubQ 1.1 Small

Subquadratic
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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

SubQ 1.1 Small is a long-context AI model from Subquadratic designed to reason over complete enterprise artifacts such as codebases, document collections, contracts, and financial filings. It uses Subquadratic Sparse Attention, or SSA, to reduce the high compute costs normally associated with processing very large context windows. The model delivers near-perfect long-context retrieval across 1M, 2M, 6M, and 12M token tests while using far less attention compute than dense attention. SubQ 1.1 Small also maintains strong general reasoning, coding, knowledge, and agentic task performance across multiple benchmarks. Its capabilities make it useful for financial analysis, legal review, contract work, software engineering, due diligence, and other workflows where information is spread across large artifacts. SubQ is built for organizations that want to move beyond fragmented retrieval pipelines and enable direct reasoning over massive bodies of information.

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

SubQ 1.1 Small is best suited for enterprises, AI teams, software engineering groups, legal teams, financial analysts, and research organizations that need efficient long-context reasoning across large documents, codebases, filings, contracts, and complex information collections

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

No information available.
Free Version
Free Trial

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
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

Subquadratic
Founded: 2026
United States
subq.ai/subq-1-1-small-technical-report

Alternatives

Alternatives

Claude Fable 5

Claude Fable 5

Anthropic
Inkling

Inkling

Thinking Machines Lab
MiniMax M3

MiniMax M3

MiniMax
SubQ

SubQ

Subquadratic

Categories

Categories

Integrations

Claude Code
Model Context Protocol (MCP)
OpenAI
OpenAI Codex
SubQ
Tinker

Integrations

Claude Code
Model Context Protocol (MCP)
OpenAI
OpenAI Codex
SubQ
Tinker
Claim Inkling-Small and update features and information
Claim Inkling-Small and update features and information
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