Inkling-Small

Inkling-Small

Thinking Machines Lab
Kimi K2

Kimi K2

Moonshot AI
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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

Kimi K2 is a state-of-the-art open source large language model series built on a mixture-of-experts (MoE) architecture, featuring 1 trillion total parameters and 32 billion activated parameters for task-specific efficiency. Trained with the Muon optimizer on over 15.5 trillion tokens and stabilized by MuonClip’s attention-logit clamping, it delivers exceptional performance in frontier knowledge, reasoning, mathematics, coding, and general agentic workflows. Moonshot AI provides two variants, Kimi-K2-Base for research-level fine-tuning and Kimi-K2-Instruct pre-trained for immediate chat and tool-driven interactions, enabling both custom development and drop-in agentic capabilities. Benchmarks show it outperforms leading open source peers and rivals top proprietary models in coding tasks and complex task breakdowns, while its 128 K-token context length, tool-calling API compatibility, and support for industry-standard inference engines.

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, 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

Audience

AI researchers, developers and organizations seeking an open source, high-performance agentic intelligence model for advanced reasoning, coding and autonomous task execution

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

Free
Open source
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:

Review this Software

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

Moonshot AI
Founded: 2023
China
moonshotai.github.io/Kimi-K2/

Alternatives

Alternatives

Claude Code

Claude Code

Anthropic
Claude Opus 4.5

Claude Opus 4.5

Anthropic
Inkling

Inkling

Thinking Machines Lab
Kimi K2 Thinking

Kimi K2 Thinking

Moonshot AI
Kimi K3

Kimi K3

Moonshot AI

Categories

Categories

Integrations

AiAssistWorks
Brokk
EaseMate AI
Kimi
Model Context Protocol (MCP)
NVIDIA TensorRT
Nebius Token Factory
Okara
OpenClaw
OpenCode
PenguinBot
PrivatClaw
Shiori
SiliconFlow
Simtheory
Tinker
Use AI

Integrations

AiAssistWorks
Brokk
EaseMate AI
Kimi
Model Context Protocol (MCP)
NVIDIA TensorRT
Nebius Token Factory
Okara
OpenClaw
OpenCode
PenguinBot
PrivatClaw
Shiori
SiliconFlow
Simtheory
Tinker
Use AI
Claim Inkling-Small and update features and information
Claim Inkling-Small and update features and information
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