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Thinking Machines Lab's Inkling introduces a significant architectural shift by prioritizing controllable thinking effort over raw parameter count as its primary value proposition for customization. Its unique encoder-free multimodal design and specific MoE routing mechanisms offer distinct advantages for researchers fine-tuning models on specialized tasks like Tinker, rather than serving as a general-purpose leader in benchmarks.

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Thinking Machines Lab Releases Inkling: A 975B-Parameter Open-Weights Multimodal MoE With 41B Active Parameters And Controllable Thinking Effort

MarkTechPostJuly 15, 20265 min read

Thinking Machines Lab released Inkling on July 15, 2026, its first model trained from scratch. The full weights ship under Apache 2.0. It is a 975B-parameter Mixture-of-Experts transformer with 41B active parameters, a 1M-token context window, and native text, image, and audio input. The lab states plainly that Inkling is not the strongest model available, open or closed. It is positioned instead as a customization base, with controllable thinking effort as the practical differentiator. The post Thinking Machines Lab Releases Inkling: A 975B-Parameter Open-Weights Multimodal MoE With 41B Active Parameters And Controllable Thinking Effort appeared first on MarkTechPost.

Thinking Machines Lab just released Inkling, their first model trained from scratch, weights are open, fine-tunable on Tinker. The lab pitches it as a base for customization. What is Inkling? Inkling is a Mixture-of-Experts transformer with 975B total parameters and 41B active. It supports a context window of up to 1M tokens. Pretraining covered 45 trillion tokens of text, images, audio, and video. Inputs accept text, images, and audio; output is UTF-8 text only. The research team also previewed Inkling-Small, a 276B-parameter MoE with 12B active parameters. It…

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