Inkling Arrives as Open-Weight Competitor

Thinking Machines Lab released its first proprietary AI model, Inkling, on Wednesday morning, July 15, 2026. The model represents the company’s bet against one-size-fits-all AI approaches through a mixture-of-experts architecture.

Inkling is an open-weight model, meaning outside developers and companies can download it and modify it directly. The system comprises 975 billion total parameters, drawing on approximately 41 billion parameters for any given task.

Training and Capabilities

Inkling was trained on 45 trillion tokens of text, image, audio, and video, and reasons natively across all three modalities. The model was trained entirely on Nvidia’s GB300 NVL72 systems.

In its development, Thinking Machines used other open-weight models, including Moonshot AI’s Kimi K2.5, to help generate some early post-training data for Inkling.

Performance and Positioning

On one benchmark, Inkling uses a third as many tokens as Nvidia’s Nemotron 3 Ultra to achieve the same coding performance. However, Thinking Machines explicitly states that Inkling is “not the strongest model available today, closed or open.”

Company Momentum

Thinking Machines now employs roughly 200 people, up from lower levels reported after departures earlier in 2026, including two co-founders who left for OpenAI in January. The rapid product launch reflects the company’s pace: OpenAI took roughly five years from founding to bring tech to market and show revenue; Anthropic took roughly three years; Thinking Machines did the same in approximately nine months.

This milestone comes following a strategic partnership with Nvidia in March 2026 to deploy a gigawatt of Vera Rubin computing capacity.

Open Models Show Financial Sector Promise

Separately, researchers from Bridgewater Associates and an unnamed company took an existing open-source model and trained it further on Bridgewater’s financial expertise. According to a joint evaluation published in late June 2026, the model achieved 84.7% on financial reasoning tests while costing roughly a fourteenth as much to run as top proprietary AI models.

Source: TechCrunch