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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 사용해 보기 요금제 보기
790,000+명의 사용자가 신뢰합니다

1.048576M tokens

컨텍스트

131,072 tokens

최대 출력

보통

속도

이럴 때 활용하세요

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 소개

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 사용 방법

1분 안에 첫 결과물까지.

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

더 나은 결과를 위한 팁

  • 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 다른 대안들

{model}이 앞서는 부분과 그렇지 않은 부분, 솔직하게 비교합니다.

vs Nemotron 3 Ultra

모델 보기

+Native multimodal input and a larger context

–Nemotron is built specifically for agent orchestration

vs Kimi K3

모델 보기

+Open weights with a million-token context

–Kimi K3 is tuned harder for agentic coding

vs Qwen 3.8 Max

모델 보기

+Multimodal by design rather than text-first

–Qwen 3.8 Max is the larger sparse model

자주 묻는 질문

채팅 그 이상 — 이미지 & 동영상

모든 NinjaChat 요금제에는 50개 이상의 모델, 완전한 이미지 스튜디오, 동영상 생성이 포함됩니다.

A real FLUX Pro Ultra outputA real Google Imagen 4 outputA real Seedream output
전체 모델 보기 →

Inkling, 그리고 50개의 모델 더.

구독 하나로 NinjaChat의 모든 모델을 이용할 수 있습니다. Inkling도 포함됩니다.

시작하기요금제 비교
Download on the App Store

더 많은 모델 둘러보기

Nemotron 3 Ultra

NVIDIA's open frontier model for agent orchestration

→

Kimi K3

네이티브 비전과 100만 컨텍스트를 갖춘 Moonshot의 최신 오픈 웨이트 플래그십

→

Qwen 3.8 Max

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

→
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