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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.

Essayer Inkling Voir les offres
Approuvé par 790,000+ utilisateurs

1.048576M tokens

Contexte

131,072 tokens

Sortie maximale

Moyen

Vitesse

Ce pour quoi vous l'utiliserez

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.

À propos de 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.

Comment utiliser Inkling

De zéro à votre premier résultat en moins d'une 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

Conseils pour de meilleurs résultats

  • 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 face aux alternatives

Comparaisons honnêtes — là où {model} excelle, et là où il ne le fait pas.

vs Nemotron 3 Ultra

Voir le modèle

+Native multimodal input and a larger context

–Nemotron is built specifically for agent orchestration

vs Kimi K3

Voir le modèle

+Open weights with a million-token context

–Kimi K3 is tuned harder for agentic coding

vs Qwen 3.8 Max

Voir le modèle

+Multimodal by design rather than text-first

–Qwen 3.8 Max is the larger sparse model

Questions fréquentes

Au-delà du chat — images et vidéo

Chaque offre NinjaChat inclut 50+ modèles, un studio d'image complet et la génération vidéo.

A real FLUX Pro Ultra outputA real Google Imagen 4 outputA real Seedream output
Voir tous les modèles →

Inkling, et 50 autres.

Un seul abonnement couvre tous les modèles de NinjaChat, Inkling compris.

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D'autres modèles à explorer

Nemotron 3 Ultra

NVIDIA's open frontier model for agent orchestration

→

Kimi K3

Le tout dernier modèle phare open-weight de Moonshot, avec vision native et contexte de 1M

→

Qwen 3.8 Max

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

→
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