Inkling

Inkling

Thinking Machines Lab
Laguna S 2.1

Laguna S 2.1

Poolside
+
+

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About

Inkling is an open-weights multimodal AI model from Thinking Machines designed as a customizable foundation model for developers, researchers, and enterprises. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, and support for context windows up to 1 million tokens. Inkling was trained from scratch on text, images, audio, and video, giving it native capabilities across reasoning, coding, agentic tool use, vision, audio, factuality, and instruction following. It is built with controllable thinking effort so users can balance performance, latency, and token efficiency for different workloads. The model is available for fine-tuning on Tinker, with playground access, API availability through ecosystem partners, and full weights published on Hugging Face. Built for customization, Inkling gives teams an open-weights base model for building domain-specific AI systems, multimodal agents, coding workflows, research tools, and more.

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

AI developers, researchers, model fine-tuning teams, enterprise AI groups, agent builders, coding tool makers, multimodal AI teams, infrastructure providers, and organizations that need open-weights models for customization, long-context reasoning, vision, audio, tool use, and specialized AI applications

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

Free
Free Version
Free Trial

Pricing

No information available.
Free Version
Free Trial

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:

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Reviews/Ratings

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

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/

Company Information

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

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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 and update features and information
Claim Inkling 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