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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 आज़माएं Plans देखें
790,000+ उपयोगकर्ताओं का भरोसा

1.048576M tokens

कॉन्टेक्स्ट

131,072 tokens

अधिकतम आउटपुट

मध्यम

गति

आप इसे किस काम के लिए इस्तेमाल करेंगे

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 के बारे में

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 का उपयोग कैसे करें

शून्य से पहले नतीजे तक, एक मिनट से भी कम में।

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

बेहतर नतीजों के लिए टिप्स

  • 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 बनाम alternatives

ईमानदार तुलना — जहाँ {model} जीतता है, और जहाँ नहीं।

Nemotron 3 Ultra से तुलना

Model देखें

+Native multimodal input and a larger context

–Nemotron is built specifically for agent orchestration

Kimi K3 से तुलना

Model देखें

+Open weights with a million-token context

–Kimi K3 is tuned harder for agentic coding

Qwen 3.8 Max से तुलना

Model देखें

+Multimodal by design rather than text-first

–Qwen 3.8 Max is the larger sparse model

अक्सर पूछे जाने वाले सवाल

Chat से आगे — images और video

हर NinjaChat plan में 50+ models, एक पूरा image studio, और वीडियो जनरेशन शामिल है।

A real FLUX Pro Ultra outputA real Google Imagen 4 outputA real Seedream output
सभी मॉडल देखें →

Inkling, और 50 अन्य।

एक ही सब्सक्रिप्शन में NinjaChat के सभी मॉडल शामिल हैं, Inkling भी।

शुरू करेंPlans compare करें
Download on the App Store

और models explore करें

Nemotron 3 Ultra

NVIDIA's open frontier model for agent orchestration

→

Kimi K3

Moonshot का नवीनतम ओपन-वेट फ्लैगशिप, नेटिव विज़न और 1M कॉन्टेक्स्ट के साथ

→

Qwen 3.8 Max

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

→
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