Inkling-Small

Inkling-Small

Thinking Machines Lab
Laguna S 2.1

Laguna S 2.1

Poolside
+
+

Related Products

  • Gemini Enterprise Agent Platform
    985 Ratings
    Visit Website
  • TrustInSoft Analyzer
    6 Ratings
    Visit Website
  • LTX
    182 Ratings
    Visit Website
  • Checksum.ai
    1 Rating
    Visit Website
  • Dialpad Support
    1,588 Ratings
    Visit Website
  • Buildium
    2,544 Ratings
    Visit Website
  • RetailEdge
    201 Ratings
    Visit Website
  • TIMi
    68 Ratings
    Visit Website
  • Fraud.net
    56 Ratings
    Visit Website
  • PackageX OCR Scanning
    48 Ratings
    Visit Website

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

Laguna S 2.1 is an open weight agentic coding model designed to pursue longer-horizon work and make effective use of reasoning. It uses a 118-billion-parameter Mixture-of-Experts architecture with 8 billion active parameters per token and supports a context window of up to one million tokens in both thinking and no-thinking modes. Its compact active size makes it suitable for complex work on local machines while remaining competitive with models many times larger on terminal, software-engineering, codebase-question-answering, and tool-use benchmarks. Laguna S 2.1 is built to keep working through difficult tasks with greater persistence, verification, and willingness to backtrack instead of declaring success too early. In demonstrated runs, it built and validated a browser rendering engine from an empty folder, optimized an agent harness for faster execution and substantially lower memory allocation, and completed extended mathematical research using the tools in its environment.

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

Private AI infrastructure teams that need a compact, self-hostable model for long-running coding and software-engineering agents

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

No information available.
Free Version
Free Trial

Reviews/Ratings

Overall 5.0 / 5
features 4.0 / 5

Reviews/Ratings

Overall 5.0 / 5
ease 5.0 / 5
features 4.0 / 5
design 5.0 / 5

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.

Pros & Cons from Real Users

Pros

  • Laguna S 2.1 looks awesome from a developer’s point of view because it is built specifically for agentic coding, not just general chatbot tasks. I like that it is open-weight, relatively compact for its capability, and designed for the kind of workflows where an AI needs to inspect a repo, reason through changes, edit code, and keep moving across multiple steps. The 1M-token context window is a huge plus. For real engineering work, context is everything: source files, docs, logs, tests, tickets, configs, and previous attempts all matter. Having a coding model that can handle that much context makes it much more useful for serious repo-level work.

Cons

  • It is still new, so I would want to test it heavily before trusting it with production code. Coding benchmarks are useful, but the real test is messy repos, weird dependencies, flaky tests, security-sensitive changes, and long-running agent loops.

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

Poolside
Founded: 2023
United States
poolside.ai/blog/introducing-laguna-s-2-1

Alternatives

Alternatives

Claude Fable 5

Claude Fable 5

Anthropic
Claude Opus 5

Claude Opus 5

Anthropic
GLM-5.2

GLM-5.2

Zhipu AI
Inkling

Inkling

Thinking Machines Lab
Laguna XS 2.1

Laguna XS 2.1

Poolside
Laguna XS.2

Laguna XS.2

Poolside

Categories

Categories

Integrations

Agent Client Protocol (ACP)
Claude Code
Cline
Hermes Agent
Hugging Face
IntelliJ IDEA
Kilo Code
Model Context Protocol (MCP)
Nous Portal
Ollama
OpenAI Codex
OpenClaw
OpenCode
OpenRouter
Poolside
Roo Code
Tinker
Visual Studio
Visual Studio Code
Zed

Integrations

Agent Client Protocol (ACP)
Claude Code
Cline
Hermes Agent
Hugging Face
IntelliJ IDEA
Kilo Code
Model Context Protocol (MCP)
Nous Portal
Ollama
OpenAI Codex
OpenClaw
OpenCode
OpenRouter
Poolside
Roo Code
Tinker
Visual Studio
Visual Studio Code
Zed
Claim Inkling-Small and update features and information
Claim Inkling-Small and update features and information
Claim Laguna S 2.1 and update features and information
Claim Laguna S 2.1 and update features and information