AI startup Thinking Machines launches an open-weight AI model

Named Inkling, the model is open-weight, meaning users can download, run and customize the underlying ‌systems, unlike proprietary, ⁠closed-source ⁠models.

AI startup Thinking Machines launches an open-weight AI model
AI startup Thinking Machines revealed on Wednesday a new artificial intelligence model that could serve as one of the few alternatives to popular open-source offerings from Chinese AI labs.

Named Inkling, the model is open-weight, meaning users can download, run and customize the underlying ‌systems, unlike proprietary, ⁠closed-source ⁠models.

It is the first general-purpose model release to come out of Thinking ​Machines, a San Francisco-based startup founded last year by OpenAI's former chief technology ​officer Mira Murati.


Thinking Machines launched its first product called Tinker, which helps customize AI models, last October. Inkling is available on ​Tinker and other developer platforms, it said.

The ⁠model has ‌975 billion parameters - variables that determine how an ​AI system ​processes information - making it one of the largest ⁠models of its kind.

The open-source ecosystem in the ​West lags behind its counterpart in China, especially ​in the wake of a void left by Meta, which changed course to a proprietary approach after the disappointing release of its open Llama 4 model last year.
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Businesses have in turn flocked to adopt Chinese models as the primary alternatives to expensive ‌closed-source models. Hedge fund Bridgewater Associates used Tinker to build a custom version of Qwen, a model developed ​by China's ​Alibaba, which it said ⁠outperformed top proprietary models at lower costs.

Thinking Machines published a series of benchmarks that compared Inkling's capabilities with closed models from Anthropic, Google, ​and OpenAI, as well as leading open offerings, most of them from Chinese labs.

While those other models maintain the edge on performance overall, Inkling put in a competitive showing, particularly on agent-related tasks, that could spur interest from prospective users.
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