# Thinking Machines 发布第二款模型 Inkling Small，主打效率而非规模

- 来源：The Decoder：AI News（RSS）
- 作者：Matthias Bastian
- 发布时间：2026-08-01 01:41
- AIHOT 分数：70
- AIHOT 标记：精选
- AIHOT 链接：https://aihot.news/items/cms98wf8g08ftro9krvlx7qfl
- 原文链接：https://the-decoder.com/thinking-machines-bets-on-efficiency-over-size-with-its-second-model-inkling-small

## 精选理由

Inkling Small 以不到三分之一的参数在多项推理测试中超越自家大模型，加上Apache 2.0开源和低token消耗，是做应用的好选择。

## AI 摘要

Thinking Machines 发布开源推理模型 Inkling Small，在 Intelligence Index 上得分 40，仅比更大的 Inkling（41）低 1 分，而总参数量不到后者的三分之一（276B 总量，12B 激活）。

## 正文

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.

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 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.

Thinking Machines