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Thinking Machines Lab

Chat with Inkling online

Thinking Machines Lab's debut — an open-weights multimodal generalist with a million-token context.

试用 Inkling 查看方案
790,000+ 位用户的信赖

1.048576M tokens

上下文窗口

131,072 tokens

最大输出

中速

速度

你会用它做什么

Very long documents

A book, an archive or a codebase in one thread.

Mixed media questions

Images and text in the same conversation, natively.

Research synthesis

Pulling one answer out of many long sources.

General reasoning

A capable generalist rather than a narrow specialist.

Prompts to steal

Tuned to this model — click any line to copy.

关于 Inkling

Inkling is Thinking Machines Lab's first public model, released on 15 July 2026, and it arrived as an open-weights release rather than a closed API — which is a statement of intent from a lab founded by people who built a lot of what the field runs on.

The architecture is a mixture-of-experts transformer with about 975 billion total parameters and 41 billion active per token, and a context window of up to a million tokens. It is natively multimodal rather than multimodal by adapter: it was pretrained on roughly 45 trillion tokens spanning text, images, audio and video, and images and video frames enter the same decoder as text rather than being translated into it. There are some genuinely unusual design choices inside, including short convolutions in every decoder block and a learned relative-position bias instead of the rotary embeddings almost everything else uses.

What that adds up to in practice is a generalist with unusual reach. A million tokens is enough for a book, a long research archive or an entire codebase in one conversation, and because the multimodality is native you can put a diagram or a recording into the same thread and ask about it directly. The lab also exposed controllable thinking effort, so the model can be pushed to reason harder on the turns that deserve it.

Thinking Machines released a lighter Inkling-Small alongside it. On NinjaChat, Inkling is included in every plan — pick it from the model list and give it something large.

如何使用 Inkling

从零到第一个结果,不到一分钟。

01

Create a NinjaChat account and choose a plan

02

Open chat and pick Inkling from the model list

03

Paste or attach the whole source, not a summary

04

Ask it to think harder on the turns that matter

获得更好结果的技巧

  • Its context is large; give it everything rather than excerpts
  • Attach images directly instead of describing them
  • Ask for citations back to the source you supplied
  • Use a Flash-class model when you want speed over depth

Inkling 与同类模型对比

客观对比——{model} 的优势所在,以及它的不足之处。

对比 Nemotron 3 Ultra

查看模型

+Native multimodal input and a larger context

–Nemotron is built specifically for agent orchestration

对比 Kimi K3

查看模型

+Open weights with a million-token context

–Kimi K3 is tuned harder for agentic coding

对比 Qwen 3.8 Max

查看模型

+Multimodal by design rather than text-first

–Qwen 3.8 Max is the larger sparse model

常见问题解答

不止于对话——图像与视频

每个 NinjaChat 方案均包含 50+ 个模型、完整图像工作室和视频生成功能。

A real FLUX Pro Ultra outputA real Google Imagen 4 outputA real Seedream output
查看所有模型 →

Inkling,还有另外 50 多个模型。

一个订阅即可使用 NinjaChat 上的所有模型,包括 Inkling。

开始使用对比方案
Download on the App Store

更多模型,等你探索

Nemotron 3 Ultra

NVIDIA's open frontier model for agent orchestration

→

Kimi K3

Moonshot 最新开源权重旗舰,原生视觉能力,100 万上下文

→

Qwen 3.8 Max

Qwen's 2.4T frontier model for long-horizon work

→
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