Thinking Machines

A dense wave of open-weight model releases—including Thinking Machines' first model Inkling, Poolside's Laguna S2.1, and Moonshot AI's Kimi K3—signals that consolidation predictions for AI labs have not materialized. Licensing terms, particularly Kimi K3's noncommercial agreement requirement, are emerging as a new front in US-China AI policy.

A wave of open-weight model releases from Thinking Machines, Tencent, Poolside, Moonshot AI, and Meituan signals that model-building capacity is spreading rather than consolidating. The releases range from a 1.6 trillion-parameter MoE trained entirely on Chinese accelerators to a noncommercial-licensed model raising new questions about US-China AI trade.

Thinking Machines has released Inkling Small, an open-weights reasoning model with 276 billion total parameters but only 12 billion active. According to Artificial Analysis, it scores nearly as high as the company's larger Inkling model while using roughly a third of the parameters and far fewer output tokens per task.

Thinking Machines Lab released Inkling, a mixture-of-experts model with 975B total parameters and 41B active parameters, trained on 45 trillion tokens across text, images, audio, and video. The Apache 2.0-licensed model supports up to 1M context and debuts alongside Inkling-Small (276B-A12B), marking what observers call the strongest US-based open-weight release to date.

Thinking Machines Lab released Inkling, a 975-billion-parameter mixture-of-experts model that uses 41 billion active parameters per task. The open-weight model was trained on 45 trillion tokens across text, image, audio, and video, marking the first public release from Mira Murati's AI startup.