Inkling-Small

Inkling-Small

Thinking Machines Lab
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About

Gemini 3.7 Flash is Google’s most intelligent workhorse model yet for coding and agents, delivering substantial improvements across software engineering, knowledge work, web development, and complex business workflows. It shows stronger performance in debugging and issue resolution, higher first-pass code accuracy, and improved generation of production-ready code. For web development, the model creates more functional layouts and feature-complete applications in fewer prompts, with strong design adherence when working from screenshots, images, or complete design systems. In knowledge-dense fields such as finance, law, and biosciences, it provides improved reasoning, accuracy, and complex-document understanding. Gemini 3.7 Flash also performs more effectively on real-world workflow automation and multimodal tasks, supporting use cases ranging from interactive web experiences and data stories to robotics and dynamically generated 3D content.

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.

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

Developers, enterprises, and engineering teams seeking to build agents, automate complex workflows, generate production-ready software, and perform multimodal tasks

Audience

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

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

$0.75 per 1M tokens (input)
$0.75/1M input and $3.75/1M output tokens
Free Version
Free Trial

Pricing

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

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Pros & Cons from Real Users

Pros

  • The biggest draw is the focus on complex software engineering, web development, and autonomous agent workflows. If it really improves planning and instruction-following, that matters a lot for coding agents that need to work across multiple steps without drifting. The lower price is a big win too. Flash models are supposed to be the workhorses, so cutting the cost while improving coding quality makes it much more appealing for high-volume use.

Cons

  • Fast models like Gemini 3.7 Flash can be great for everyday development, but I would not hand it a production repo and blindly accept every change.

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

Google
Founded: 1998
United States
google.com

Company Information

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

Alternatives

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Grok 4.6

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Alternatives

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Claude Opus 5

Anthropic
Claude Fable 5

Claude Fable 5

Anthropic
Inkling

Inkling

Thinking Machines Lab

Categories

Categories

Integrations

.NET
Android Studio
Bind AI
C++
CSS
Devin Desktop
Gemini Computer Use
Gemini Enterprise Agent Platform Notebooks
Gemini Spark
Google AI Studio
HTML
Java
Kubernetes
OpenClaw
PowerShell
Python
R
Scala
Solidity
TypeScript

Integrations

.NET
Android Studio
Bind AI
C++
CSS
Devin Desktop
Gemini Computer Use
Gemini Enterprise Agent Platform Notebooks
Gemini Spark
Google AI Studio
HTML
Java
Kubernetes
OpenClaw
PowerShell
Python
R
Scala
Solidity
TypeScript
Claim Gemini 3.7 Flash and update features and information
Claim Gemini 3.7 Flash and update features and information
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