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  3. Inkling

Thinking Machines Lab

Chat with Inkling online

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

جرّب Inkling استعرض الخطط
يثق به 790,000+ مستخدم

1.048576M tokens

السياق

131,072 tokens

أقصى إخراج

متوسط

السرعة

ما الذي ستستخدمه من أجله

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.

حول 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.

كيفية استخدام Inkling

من الصفر إلى نتيجتك الأولى في أقل من دقيقة.

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

نصائح لنتائج أفضل

  • 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 مقابل البدائل

مقارنات موضوعية — أين يتفوق {model} وأين يقصر.

مقابل Nemotron 3 Ultra

عرض النموذج

+Native multimodal input and a larger context

–Nemotron is built specifically for agent orchestration

مقابل Kimi K3

عرض النموذج

+Open weights with a million-token context

–Kimi K3 is tuned harder for agentic coding

مقابل Qwen 3.8 Max

عرض النموذج

+Multimodal by design rather than text-first

–Qwen 3.8 Max is the larger sparse model

الأسئلة الشائعة

أبعد من الدردشة — الصور والفيديو

تتضمن كل خطط NinjaChat أكثر من 50 نموذجاً واستوديو صور متكاملاً وتوليد فيديو.

A real FLUX Pro Ultra outputA real Google Imagen 4 outputA real Seedream output
استعرض جميع النماذج →

Inkling، و50 نموذجًا آخر.

اشتراك واحد يشمل كل النماذج على NinjaChat، ومنها Inkling.

ابدأ الآنقارن الخطط
Download on the App Store

نماذج أخرى للاستكشاف

Nemotron 3 Ultra

NVIDIA's open frontier model for agent orchestration

→

Kimi K3

أحدث نموذج رائد مفتوح الأوزان من Moonshot برؤية أصلية وسياق مليون توكن

→

Qwen 3.8 Max

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

→
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