Thinking Machines amps up its bet against one-size-fits-all AI with its first open model, Inkling
It's the company's first public proof point after a year and a half spent building AI infrastructure largely out of public view. 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.
Key Takeaways
- 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.
- 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.
- " 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.
- ) 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.
- Frontier models, he said, will increasingly be reserved for experimentation and high-value tasks, while most production AI work shifts to private or open-source alternatives - the exact split Thinking Machines is building around.
Stats & Key Facts
- #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.
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.
For more details please read the original article at TechCrunch AI.
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