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2026-08-01 0 浏览 会员

Thinking Machines押注效率而非规模,推出第二款模型Inkling Small

Thinking Machines发布开源推理模型Inkling Small,以不到三分之一的参数接近Inkling的性能,在多项测试中反超,且token效率大幅提升,定位为可微调的基础模型。

SOURCE / AI小生意项目库 MIN / 9 ACCESS / 会员 POST / 2026-08-01 01:41:50

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作者:Matthias Bastian 来源站点:the-decoder.com 原贴时间:

原文

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 for Deepseek V4 Flash and 78K for GPT-5.4 mini. The model handles text, image, and speech inputs, has a 256K-token context window, and ships under Apache 2.0. Weights are on Hugging Face , and users can fine-tune it in the browser via Tinker Playground . Thinking Machines positions its models as a foundation for fine-tuning with users' own data . Some see this as the next frontier in AI . Ad DEC_D_Incontent-1 Ad Subscribe to THE DECODER for ad-free reading, a weekly AI newsletter, our exclusive "AI Radar" frontier report six times a year, full archive access, and access to our comment section.

中文翻译

Thinking Machines,这家由前OpenAI CTO Mira Murati创办的AI实验室,发布了Inkling Small。据Artificial Analysis称,这个开放权重推理模型在智能指数上得分为40,比Inkling(41)低1分,而参数不到其三之一(总参数2760亿,激活参数120亿)。AA表示,同等或更小规模的开源模型没有得分更高的。Inkling Small在多项编码和推理测试中胜过其更大的兄弟型号,包括Humanity's Last Exam(32%对30%)和GPQA Diamond(89%对87%)。它在基于智能体的任务和事实知识上表现落后,但token效率要高得多,每个任务平均输出24K个token,而Deepseek V4 Flash为45K,GPT-5.4 mini为78K。该模型处理文本、图像和语音输入,具有256K token的上下文窗口,并以Apache 2.0许可发布。权重可在Hugging Face上获取,用户可以通过Tinker Playground在浏览器中微调模型。Thinking Machines将其模型定位为用户使用自己数据进行微调的基础。一些人认为这是AI的下一个前沿。

核心信息

Thinking Machines发布开源推理模型Inkling Small,以不到三分之一的参数接近Inkling的性能,在多项测试中反超,且token效率大幅提升,定位为可微调的基础模型。

  • Thinking Machines发布开源推理模型Inkling Small,以不到三分之一的参数接近Inkling的性能,在多项测试中反超,且token效率大幅提升,定位为可微调的基础模型。
  • 原贴提到:Thinking Machines, the AI lab from former OpenAI CTO Mira Murati, has re
  • 来源:the-decoder.com
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