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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.

Inklingを試す プランを見る
790,000+人のユーザーに信頼されています

1.048576M tokens

コンテキスト

131,072 tokens

最大出力

普通

速度

こんな用途に最適

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.

Inklingについて

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.

Inklingの使い方

ゼロから最初の結果まで、1分もかかりません。

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

より良い結果を得るためのヒント

  • 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と他のモデルを比較

正直な比較 — {model}が優れている点と、そうでない点。

Nemotron 3 Ultraと比較

モデルを見る

+Native multimodal input and a larger context

–Nemotron is built specifically for agent orchestration

Kimi K3と比較

モデルを見る

+Open weights with a million-token context

–Kimi K3 is tuned harder for agentic coding

Qwen 3.8 Maxと比較

モデルを見る

+Multimodal by design rather than text-first

–Qwen 3.8 Max is the larger sparse model

よくある質問

チャット以外も — 画像・動画生成

全NinjaChatプランに50以上のモデル、本格的な画像スタジオ、動画生成が含まれています。

A real FLUX Pro Ultra outputA real Google Imagen 4 outputA real Seedream output
全モデルを見る →

Inkling、そしてさらに50以上。

1つのサブスクリプションでNinjaChatのすべてのモデルが使えます。Inklingも含まれます。

始めるプランを比較する
Download on the App Store

他のモデルも見る

Nemotron 3 Ultra

NVIDIA's open frontier model for agent orchestration

→

Kimi K3

ネイティブ視覚認識と100万トークンコンテキストを備えたMoonshot最新のオープンウェイト旗艦モデル

→

Qwen 3.8 Max

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

→
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