Thinking Machines Lab has released its first foundation model, Inkling, with full open weights and developer fine-tuning through Tinker, SiliconANGLE reported.

The release gives the AI startup a public model after a year in which its funding rounds and Nvidia partnership drew most of the attention.

The model is not being presented as a closed chatbot.

Developers can download, adjust and run the weights, and Tinker remains the paid service for fine-tuning open-weights models.

Inkling Uses 975 Billion Parameters And Open Weights

The company's blog post described Inkling as a mixture-of-experts model with 975 billion parameters.

The same model description said an average prompt draws on about 41 billion parameters to process tasks faster and keep costs low.

The training description said Inkling used about 45 trillion tokens spanning text, image, audio and video.

The model can reason across all four inputs, but its outputs are limited to text, including code, styled artifacts and structured data.

Full open weights let developers inspect and adapt the model code.

Thinking Machines also described thinking-effort controls for trading processing speed against accuracy, and the outlet said the model flags uncertainty in outputs.

Mira Murati previously served as Chief Technology Officer of OpenAI before leaving in September 2024, according to the account.

Her stated focus on accessibility, customisation and multimodal collaboration appears in the launch, with public outputs still limited to text.

Tinker Carries The Fine-Tuning Revenue Model

Developers can fine-tune the model directly on Tinker, the startup's training API that launched in October, according to the outlet.

The paid API carries the revenue plan for the release instead of a metered model-access charge.

The training path also carries the Nvidia connection.

The company said the model was trained on Nvidia's GB300 NVL72 system under a partnership announced in March.

In company-cited early test results, the startup said the model reached comparable coding performance with Nvidia's Nemotron 3 Ultra while using two-thirds less tokens.

Independent benchmark methodology and customer deployment results are not included in that comparison.

Bridgewater Test Gives Inkling A Finance Example

The account cited a collaboration with Bridgewater Associates in which researchers used Tinker to fine-tune an open model with specialised financial data.

The resulting lightweight model scored 84.7% on financial reasoning benchmarks at less than 10% of the cost of advanced proprietary alternatives, the account said.

Futurum Group analyst Mitch Ashely told the Wall Street Journal, as cited in the account, that the open-weight model ecosystem had been dominated by Chinese AI firms for the last year.

The quoted assessment described the release as a Western alternative for enterprises weighing customisation economics and infrastructure control.

The lab acknowledged that its new model is not as strong as some advanced proprietary AI systems.

The release is positioned as a base model that organisations can fine-tune and run on their own infrastructure, not as a rigid chatbot application.

The company said it developed the model from scratch in less than nine months.

The account does not name first enterprise customers, Tinker pricing, deployment counts or independent benchmark validation for the new model.