Thinking Machines bets on efficiency over size with its second model, Inkling Small

Thinking Machines bets on efficiency over size with its second model, Inkling Small

Thinking Machines, the AI lab from former OpenAI CTO Mira Murati, has released Inkling Small.According to Artificial Analysis, the open-weights reasoning model scores 40 on the Intelligence Index, one point below Inkling (41), with less than a third of the parameters (276 billion total, 12 billion active). AA says no open model of equal or smaller size scores higher.

Inkling Small beats its bigger sibling on several coding and reasoning tests, including Humanity's Last Exam (32% vs. 30%) and GPQA Diamond (89% vs. 87%). It falls behind on agent-based tasks and factual knowledge but is far more token-efficient, averaging 24K output tokens per task compared to 45K forDeepseek V4 Flashand 78K for GPT-5.4 mini.

Mira Murati's Thinking Machines ships a smaller, more efficient reasoning model that punches above its weight. | Image: Artificial Analysis

The model handles text, image, and speech inputs, has a 256K-token context window, and ships under Apache 2.0. Weights are onHugging Face, and users can fine-tune it in the browservia Tinker Playground. Thinking Machines positions its models as a foundation forfine-tuning with users' own data. Some see this asthe next frontier in AI.

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