Mira Murati’s Thinking Machines drops Inkling, an open-weights model anyone can access

Mira Murati’s Thinking Machines Lab Inc. today launched its first foundation model with the release of Inkling, making its full open weights available to developers so they can fine-tune it as they wish.

Inkling is the first model fully trained from scratch by Thinking Machines, coming after a year in which the company mostly made headlines for its sizable funding rounds and its partnership with Nvidia Corp.

In a blog post, Thinking Machines explained that Inkling is a mixture-of-experts model that features 975 billion parameters, although for the average prompt it will only draw on a small fraction of that number – about 41 billion – in order to process tasks faster and keep costs low.

The company said the model was trained on about 45 trillion tokens of text, image, audio and video and can reason natively across all four inputs. However, its outputs are limited to text only, though that includes code, styled artifacts and structured data.

The launch of Inkling suggests that Thinking Machines wants to provide the growing number of Western companies embracing lower-cost Chinese AI models with an alternative to those systems. That’s because the model seemingly fills a gap in the Western open-source AI ecosystem, which has lagged far behind that of China’s. That gap has only increased since Meta Platforms Inc. downplayed its Llama family of models in favor of a more proprietary approach with its latest AI systems.

Murati, who was previously the chief technology officer of OpenAI Group PBC before leaving in September 2024, has long insisted that her new company is all about accessibility, customization and multimodal collaboration, and that’s clearly apparent with Inkling. Because Inkling is available to download with its full open weights, it means developers can look at the full codebase of the model, and tweak it for different use cases, without having to pay expensive licensing fees.

It also features “thinking effort” controls that allow developers to make tradeoffs, such as sacrificing processing speed for accuracy. Uniquely, the model will also flag its outputs for uncertainty, instead of simply pushing out hallucinations.

Developers can fine-tune the model directly on Tinker, which is the company’s training application programming interface that launched in October. In its early test results, Thinking Machines showed that Inkling was able to achieve a comparable coding performance with Nvidia’s Nemotron 3 Ultra model, despite using two-thirds fewer tokens.

Our first model, Inkling. Trained from scratch, weights are open, fine-tunable on Tinker today. https://t.co/m7q5RsX0Ud

— Mira Murati (@miramurati) July 15, 2026

Futurum Group analyst Mitch Ashely told the Wall Street Journal that the open-weight model ecosystem has been dominated by Chinese AI firms for the last year, and that Inkling is the first Western alternative to those systems. “It gives Western enterprises a credible alternative positioned on customization economics, shifting spend from per-token API pricing to infrastructure the enterprise controls,” he said. “Engineering teams should treat base-model selection as an architecture decision. The model an organization fine-tunes becomes part of its software substrate and switching costs compound with every downstream customization. That evaluation cannot be deferred.”

Thinking Machines acknowledged that Inkling isn’t as strong as some of the most advanced proprietary AI systems available, but it’s clearly betting that its customizability will make up for that. Instead of making Inkling available in a rigid chatbot-style app, it’s positioned as a base model that organizations should fine-tune and run themselves on their own infrastructure.

It’s a strategy that should appeal to many organizations. In a collaboration with Bridgewater Associates LP, researchers used the Tinker platform to fine-tune an open model with specialized financial data. They ended up with a low-cost, lightweight model that scored an impressive 84.7% on leading financial reasoning benchmarks, outperforming the most advanced proprietary alternatives at less than 10% of the cost.

Thinking Machines said it was able to develop Inkling from scratch in less than nine months, far less than the multiyear development timelines seen at rivals like OpenAI and Anthropic PBC. In its post on X announcing the model, the company explained it was trained on Nvidia’s GB300 NVL72 system under a partnership between the two firms that was announced in March.

Instead of charging customers for access via a metered API, Thinking Machines plans to generate revenue through Tinker, which is a paid service that makes it simple for developers to fine-tune open-weights models for specific tasks. It should be a key test that will show whether open-weight AI models have what it takes to disrupt the gated, paid access model pioneered by Silicon Valley’s biggest AI firms.

Constellation Research analyst Holger Mueller told SiliconANGLE that Thinking Machines’ business model could well prove to be the biggest innovation here, rather than the Inkling model.

“Unlike its rivals, which charge for model access, Thinking Machines is charging for Tinker, the platform that companies will likely want to use to customize Inkling for their specific use cases,” Mueller said. “If it’s successful, this will further accelerate the commoditization of large language models, and that’s something that businesses are going to welcome, because it means they’ll see an ROI on their AI investments much more easily. This could really shake up the AI industry.”

Image: Thinking Machines

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