Thinking Machines Releases First Open AI Model, Inkling

Thinking Machines Lab, the AI startup founded by former OpenAI CTO Mira Murati, released its first in-house AI model Wednesday morning, called Inkling. And unlike the flagship models from OpenAI, Anthropic, or Google, it's open-weight, meaning outside developers and companies can download it and modify it directly. Inkling is a mixture-of-experts system with 975 billion total parameters, though it only draws on a fraction of that — about 41 billion — for any given task, a common design that keeps very large models faster and cheaper to run.

It was trained on 45 trillion tokens of text, image, audio, and video, and reasons natively across all four, according to the company's own release materials. For now, though, its outputs are limited to text, including code, styled artifacts, and structured data. The model is Thinking Machines Labs' first public proof point after a year and a half spent building AI infrastructure largely out of public view.

Some of that work had already surfaced in a May research preview of "interaction models" — AI designed to listen and speak (and even interrupt) instead of stop and wait as with typical chatbots. It's also a test of the central bet behind the startup, which is that AI that organizations can adapt for themselves will outperform the one-size-fits-all models the biggest labs currently sell. Inkling is designed to give calibrated answers, including flagging uncertainty rather than guessing, and lets users dial "thinking effort" up or down when they want to trade for speed.

On one benchmark, the company says, Inkling uses a third as many tokens as Nvidia's Nemotron 3 Ultra — its latest generation open-weight model — to hit the same coding performance. Thinking Machines doesn't claim Inkling is best-in-class. Its briefing materials state explicitly that Inkling is "not the strongest model available today, closed or open." What it's evidently going for instead is well-rounded performance and customizability.

That raises the question of who, within the enterprise market it's targeting, this product is really for. Thinking Machines is, for now, marketing Inkling less as a finished product than as a starting point, something for organizations to fine-tune themselves through Tinker, the company's model-customization platform. This also means customers, not Thinking Machines, are responsible for making sure their customizations are safe, for example.

(Fine-tuning requires serious machine-earning talent.) OpenAI, Anthropic, and Google have all taken a very different approach with ChatGPT, Claude, and Gemini, respectively, which were all built to compete as general-purpose chatbots first, with agentic, autonomous features layered on top. A post published by Thinking Machines last week was clearly meant as the backdrop for this release. AI that's trained centrally by one company and then set in stone, the company argued in that post, underperforms AI that organizations shape themselves because so much expertise is specific to the people who hold it.