Inkling-Small

Inkling-Small

Thinking Machines Lab
5.0 out of 5 stars
AfterQuery Inkling-Small

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AfterQuery:

About

AfterQuery is an applied research platform designed to create high-quality training data for frontier artificial intelligence models by capturing how real experts think, reason, and solve problems in professional contexts. It focuses on transforming real-world work into structured datasets that go beyond simple outputs, encoding decision-making processes, tradeoffs, and contextual reasoning that traditional internet-sourced data cannot provide. It works directly with domain experts to generate supervised fine-tuning data, including prompt–response pairs and detailed reasoning traces, as well as reinforcement learning datasets with expert-designed prompts and grading frameworks that convert subjective judgment into scalable reward signals. It also builds custom agent environments across APIs and tools, enabling models to be trained and evaluated in realistic workflows, and captures computer-use trajectories that demonstrate how humans interact with software step by step.
Inkling-Small:

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.
AfterQuery:

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
Inkling-Small:

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
AfterQuery:

Audience

AI researchers, developers, and enterprises building advanced models who need high-quality, expert-driven training data to improve reasoning and real-world task performance
Inkling-Small:

Audience

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

Support

Phone Support Not Supported
24/7 Live Support Not Supported
Online Supported
Inkling-Small:

Support

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

API

Offers API Supported
Inkling-Small:

API

Offers API Supported
AfterQuery:

Screenshots and Videos

Inkling-Small:

Screenshots and Videos

AfterQuery:

Pricing

No information available.
Free Version Not Supported
Free Trial Not Supported
Inkling-Small:

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
AfterQuery:

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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Inkling-Small:

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5
Inkling-Small:

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.
AfterQuery:

Training

Documentation Supported
Webinars Not Supported
Live Online Not Supported
In Person Not Supported
Inkling-Small:

Training

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

Company Information

AfterQuery
Founded: 2025
United States
www.afterquery.com
Inkling-Small:

Company Information

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

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Inkling-Small:

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AfterQuery:

Categories

Inkling-Small:

Categories

AfterQuery:

Integrations

Model Context Protocol (MCP) Supported
Tinker Not Supported
Inkling-Small:

Integrations

Model Context Protocol (MCP) Supported
Tinker Supported
AfterQuery:
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Claim AfterQuery and update features and information
Inkling-Small:
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