Inkling-Small

Inkling-Small

Thinking Machines Lab
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About

Gemini 3.6 Flash is Google’s newest Flash model built for efficient, reliable, production-scale AI agents. The model improves on Gemini 3.5 Flash with stronger coding, knowledge work, multimodal performance, computer use, and agentic workflow execution. Gemini 3.6 Flash is designed to use fewer output tokens, take fewer reasoning steps, reduce unnecessary tool calls, and lower the cost of complex AI tasks. It supports document parsing, chart analysis, data analysis, report drafting, code migrations, visual understanding, and multi-agent orchestration. The model is available through the Gemini API, Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise Agent Platform, Gemini Enterprise app, and the Gemini app. Built for developers and enterprises, Gemini 3.6 Flash helps teams build faster, lower-cost, and more capable AI agents across coding, analysis, productivity, and multimodal workloads.

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, AI agent builders, enterprise AI teams, software engineering teams, data analysts, knowledge workers, product teams, and organizations that need efficient coding support, multimodal reasoning, computer use, document analysis, agentic workflows, lower token usage, and production-scale Gemini API access

Audience

Developers, AI agent builders, software engineering teams, research teams, enterprise AI teams, multimodal application developers, coding assistant builders, tool-use workflow teams, and organizations that need efficient reasoning, long-context processing, text-image-audio understanding, adjustable thinking effort, coding performance, and scalable Mixture-of-Experts inference

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

$1.50 per 1M tokens (input)
$1.50/1M input tokens and $7.50/1M 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

  • Gemini 3.6 Flash feels like a really solid upgrade from a developer’s point of view. I like that Google is not just chasing “bigger model” headlines here, but focusing on the stuff that matters when you are actually building: coding quality, speed, cost, and token efficiency. The 17% fewer output tokens claim is a big deal for developers running agents, coding assistants, or high-volume workflows. When a model is being called over and over for planning, code edits, summaries, tool calls, and debugging loops, small efficiency gains can turn into real savings.

Cons

  • The main downside is that Flash still sounds like the efficient model, not the absolute top-end reasoning model. For really hard architecture work, long autonomous coding runs, or deep research-heavy tasks, I would still want to test it against the strongest frontier models before making it my default.

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
gemini.google.com

Company Information

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

Alternatives

Claude Fable 5

Claude Fable 5

Anthropic

Alternatives

Claude Mythos 5

Claude Mythos 5

Anthropic
Claude Opus 5

Claude Opus 5

Anthropic
Inkling

Inkling

Thinking Machines Lab
Gemini 4

Gemini 4

Google

Categories

Categories

Integrations

Bind AI
C++
Factory Droid
Gemini 3.5 Flash-Lite
Gemini Enterprise
Google AI Mode
Google AI Studio
Google AI Ultra
Google Antigravity
HTML
Kubernetes
Model Context Protocol (MCP)
Objective-C
OfoxAI
OpenClaw
R
Ruby
Rust
TypeScript
Vercel AI Gateway

Integrations

Bind AI
C++
Factory Droid
Gemini 3.5 Flash-Lite
Gemini Enterprise
Google AI Mode
Google AI Studio
Google AI Ultra
Google Antigravity
HTML
Kubernetes
Model Context Protocol (MCP)
Objective-C
OfoxAI
OpenClaw
R
Ruby
Rust
TypeScript
Vercel AI Gateway
Claim Gemini 3.6 Flash and update features and information
Claim Gemini 3.6 Flash and update features and information
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