Inkling-Small

Inkling-Small

Thinking Machines Lab
+
+

Related Products

  • Gemini Enterprise Agent Platform
    985 Ratings
    Visit Website
  • Google AI Studio
    30 Ratings
    Visit Website
  • LM-Kit.NET
    29 Ratings
    Visit Website
  • Innoslate
    93 Ratings
    Visit Website
  • RaimaDB
    12 Ratings
    Visit Website
  • Evertune
    1 Rating
    Visit Website
  • Planview Software Product Delivery
    2 Ratings
    Visit Website
  • LTX
    182 Ratings
    Visit Website
  • Coevera
    752 Ratings
    Visit Website
  • Macaw AMS
    8 Ratings
    Visit Website

About

GPT-5.6 Luna is the fast and affordable model in OpenAI’s GPT-5.6 series, built to bring strong capability to users and developers who need practical intelligence with lower overhead. In the new GPT-5.6 naming system, the number identifies the model generation, while Sol, Terra, and Luna identify durable capability tiers that can advance on their own cadence, giving people and developers clearer choices across intelligence, speed, and cost. Luna sits alongside Sol, the flagship model, and Terra, the balanced model for everyday work, as part of a family designed for broader access to next-generation AI. During the limited preview, GPT-5.6 models are initially available through the API and Codex to a select group of trusted partners and organizations, with plans for broader availability in ChatGPT, Codex, and the API. OpenAI developed GPT-5.6 Sol, Terra, and Luna with its most robust safeguards to date, with configurations matched to each model’s capabilities.

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 and product teams that need a fast model for everyday AI features, coding support, API workflows, and scalable assistant experiences

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

$0.20 per 1M tokens (input)
$0.20 input / $1.20 output per 1 million 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
features 4.0 / 5
design 5.0 / 5

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Pros & Cons from Real Users

Pros

  • GPT-5.6 Luna is the one I would use when speed and cost matter most. As a developer, I can see it being really useful for lightweight coding help, quick explanations, small refactors, simple scripts, test generation, issue triage, and agent steps that do not need the most expensive model in the stack. I also like Luna for multi-agent systems where a lot of small tasks need to run in parallel. OpenAI describes Luna as the fastest and most cost-efficient GPT-5.6 model, and that is exactly the kind of model developers need for high-volume workflows.

Cons

  • Luna is not the model I would choose for the hardest reasoning tasks. If I were debugging a complicated distributed system, planning a major architecture change, or running a long autonomous coding agent, I would probably step up to Terra or Sol. It also depends heavily on good task routing. Luna can be extremely valuable when used for the right jobs, but expecting it to perform like the flagship model on every problem would be the wrong approach.

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

OpenAI
Founded: 2015
United States
openai.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
GPT-5.6 Sol

GPT-5.6 Sol

OpenAI

Categories

Categories

Integrations

Anuma
Augment Code
CSS
ChatGPT Go
ChatGPT Pro
Codex CLI
Cursor
GPT-5.4 nano
GPT-5.5-Cyber
Gemini Enterprise Agent Platform
Hermes Agent
Kineto by JetBrains
LaunchLemonade
Lovable
Microsoft Copilot Studio
R
React
Tinker
Use AI
Yonoo

Integrations

Anuma
Augment Code
CSS
ChatGPT Go
ChatGPT Pro
Codex CLI
Cursor
GPT-5.4 nano
GPT-5.5-Cyber
Gemini Enterprise Agent Platform
Hermes Agent
Kineto by JetBrains
LaunchLemonade
Lovable
Microsoft Copilot Studio
R
React
Tinker
Use AI
Yonoo
Claim GPT-5.6 Luna and update features and information
Claim GPT-5.6 Luna and update features and information
Claim Inkling-Small and update features and information
Claim Inkling-Small and update features and information