“We believe in keeping the weirdness alive.” That line comes from a manifesto Mira Murati’s lab published last week. It is also the thinking behind the lab’s first model.
Thinking Machines Lab, founded by the former OpenAI chief technology officer, has released Inkling. It is open-weight, so any developer or company can download the model and reshape it. That alone sets it apart from the flagships sold by OpenAI, Anthropic, and Google.
Inkling is big. It is a mixture-of-experts system with 975 billion total parameters, though it uses only about 41 billion for any given task. It handles a context window of up to 1 million tokens, and it trained on 45 trillion tokens of text, images, audio, and video. It reasons across text, images, and audio, but for now it only writes text back, including code and structured data.
A model that admits it is not the best
Here is the twist. Thinking Machines does not claim Inkling tops the charts. Its own materials call it “not the strongest model available today, closed or open.”
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The lab is chasing something else: range and adaptability. Thinking Machines Inkling is meant to be a broad, balanced base that organisations fine-tune for their own work, not a finished chatbot. Users can dial its “thinking effort” up or down to trade accuracy for speed. On one coding test, the company says, Inkling matches Nvidia’s Nemotron 3 Ultra using a third as many tokens.
The lab also previewed a lighter model, Inkling-Small, with 12 billion active parameters. It suits jobs where cost and speed matter most.
The bet: shape it yourself
The whole release rests on one wager. AI trained in one place and then frozen, the lab argues, loses to AI that each organisation can shape around its own expertise. Customers fine-tune Inkling through Tinker, Thinking Machines’ customisation platform, and they own the result. They also carry the safety risk of whatever they build.
The lab points to a project with the hedge fund Bridgewater as proof. The two trained an open model on Bridgewater’s financial know-how, and it scored 84.7% on financial reasoning tests, beating top proprietary models at a fraction of the cost. That figure comes from the two companies’ own evaluation, not an independent one.
The argument is gaining company. Microsoft’s Satya Nadella recently warned that firms using closed models pay twice, once in fees and once by handing over the knowledge baked into their prompts. Cheap open-weight models, many from China, pull the same way.
Nine months, with some borrowed help
Thinking Machines is keen to stress its speed. OpenAI took about five years to ship and earn, and Anthropic roughly three, TechCrunch noted. Murati’s lab says it did it in about nine months.
It cut a few corners to get there. To start Inkling’s training, the lab leaned on other open models, including Moonshot’s Kimi K2.5, a practice known as distillation. Its next model, it insists, will train fully on its own. Inkling ran on Nvidia’s GB300 systems, part of a March deal for a gigawatt of Nvidia compute.
Money and people have been bumpier. The lab raised $2bn at a $12bn valuation last year, and a reported $50bn round stalled. Two co-founders left earlier this year, though headcount is back to around 200. For now, Thinking Machines will not charge for Inkling at all. Its money comes from Tinker, and its case rests on the weirdness holding up.
