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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 ausprobieren Tarife ansehen
Vertraut von 790,000+ Nutzern

1.048576M tokens

Kontext

131,072 tokens

Max. Ausgabe

Mittel

Geschwindigkeit

Wofür du es nutzen wirst

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.

Über 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.

So nutzen Sie Inkling

Von null zum ersten Ergebnis in unter einer Minute.

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

Tipps für bessere Ergebnisse

  • 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 vs. die Alternativen

Ehrliche Vergleiche – wo {model} überzeugt und wo nicht.

vs. Nemotron 3 Ultra

Modell ansehen

+Native multimodal input and a larger context

–Nemotron is built specifically for agent orchestration

vs. Kimi K3

Modell ansehen

+Open weights with a million-token context

–Kimi K3 is tuned harder for agentic coding

vs. Qwen 3.8 Max

Modell ansehen

+Multimodal by design rather than text-first

–Qwen 3.8 Max is the larger sparse model

Häufig gestellte Fragen

Mehr als Chat – Bilder & Video

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A real FLUX Pro Ultra outputA real Google Imagen 4 outputA real Seedream output
Alle Modelle ansehen →

Inkling — und 50 weitere.

Ein Abo deckt jedes Modell auf NinjaChat ab, Inkling inklusive.

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Weitere Modelle entdecken

Nemotron 3 Ultra

NVIDIA's open frontier model for agent orchestration

→

Kimi K3

Moonshots neuestes Open-Weight-Flaggschiff mit nativer Bildverarbeitung und 1M Kontext

→

Qwen 3.8 Max

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

→
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