Thinking Machines Lab Releases Open-Weights Model Inkling

ArticleThinking Machines LabAnnouncement

Thinking Machines Lab has introduced Inkling, an open-weights Mixture-of-Experts model featuring 975B total parameters (41B active) and a 1M token context window. It is built as a generalist foundation optimized for customization via the Tinker platform.

Key points

  1. Inkling is an MoE transformer with 975B total parameters, 41B active parameters, and a context window of up to 1M tokens.
  2. Pretrained on 45 trillion tokens across text, images, audio, and video, it features controllable thinking effort.
  3. Inkling-Small was previewed as a lighter variant with 12B active parameters for lower cost and latency.
  4. In a self-fine-tuning demo inside OpenCode, Inkling prepared datasets, ran a training job on Tinker, and updated its own weights to follow a lipogram constraint avoiding the letter 'e'.
  5. Inkling scored 1257 on the Design Arena Agentic Web Dev leaderboard, positioning it among top-tier open-weights models.

Why it matters

Inkling provides engineers with an adaptable open-weights model capable of large-context multimodal reasoning. Direct integration with Tinker's fine-tuning and playground tooling makes domain adaptation more accessible without relying solely on closed APIs.

Analysis

Instead of chasing raw benchmark supremacy against closed frontier models like Claude Sonnet 5 or GPT-5.6 Sol, Thinking Machines Lab emphasizes balanced generalist breadth and post-training adaptability. The self-tuning pipeline highlights strong agentic coding capabilities and an architecture ready for automated, programmatic fine-tuning workflows.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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