Today, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available. Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.

Congrats to @thinkymachines on the release of their open weight model Inkling! We were proud to work with their incredible team on preparing this model for release for the past several months. Now live on our MCP Atlas and AudioMultiChallenge leaderboards. Inkling tied for 🥇 on AudioMultiChallenge, surpassing Gemini 3 Pro as the de facto frontier model that supports native audio input. Also notable, on MCP Atlas Inkling had a low hallucination rate compared to other frontier models.

Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵

We started Thinking Machines a year and a half ago with a couple of instincts: that people should have much more ability to customize models and do research on them, and that even as AI becomes more autonomous, there's a lot more to build to make humans and AIs work well together. A lot has happened since then, especially the massive progress in agents, so we wanted to revisit those instincts in light of everything we've learned, argue about them, and write down what we actually believe now. This is where we landed after a lot of debate. I'm happy with it!

We're building AI that people and organizations can shape and make their own. AI should extend our will and judgment instead of neglecting it; enabling that is the technical challenge we are working to solve.

1. (System design) - The Interaction Models see your screen and collaborates with you live. Here we're building a scalable system architecture together — no copy-pasting, no switching tabs, just thinking out loud and drawing on the screen together.

we are excited to share our latest work on interactive human-AI collaboration! as intelligence increases, we think progress will be bottlenecked by the ability of AI to work *with* humans -- thereby enabling AI to positively impact the long tail of human experiences

There's been a lot of excitement around auto-research, but one underappreciated bottleneck: coding agents struggle to run LLM training jobs at scale. A small infrastructure mistake can have major consequences on the output. I recently joined @thinkymachines, and @tinkerapi solves exactly this. It standardizes the training process — training a 1T parameter model is as simple as training a 4B one. That makes auto-research with coding agents like Claude Code actually viable.

Grateful to Jensen and @nvidia team for their support. Together, we’re working to deploy at least 1GW of Vera Rubin systems, bringing adaptable collaborative AI to everyone.

encouraging progress on continued test-time adaptation beyond model deployment! very excited about the future of personalized models, and developing reliable, easy-to-use pipelines to enable robust & personalized intelligence i think the "no TTT" baseline from Section 4.5 is particularly neat, justifying training with gradient steps at test time

Introducing Large Video Planner (LVP-14B) — a robot foundation model that actually generalizes. LVP is built on video gen, not VLA. As my final work at @MIT, LVP has all its eval tasks proposed by third parties as a maximum stress test, but it excels!🤗

in the past couple months of closed beta, Tinker has been used to solve Putnam, has powered our blog posts, and has been accelerating internal research! excited to see the innovation from making trillion-parameter RL broadly available -- Tinker is a dream for multi-agent setups, personalization, and continual adaptation

On-policy distillation would revolutionize multi-turn tool-use training beyond RL, but neither Tinker nor TRL which implements on-policy supports anything other than single-turn distillation. We therefore have taken this upon ourselves and implemented this feature in native Tinker. Specifically, with a trainable Tinker client, a model can now call a list of tools, interact with tool results for multiple turns, and return tokens, logprobs, and reward masks sufficient for a distillation training job (p1-2). The engineering we have achieved is to implement tool calling and parsing for Tinker models, which lies in @thinkymachines 's TODO list in their tinker_cookbook code (p3). Apart from that, we also create a dedicated inference stream that spins up robust, multi-turn tool loop that can run alongside a training job and sync the weights in real time. It becomes easy to write a simple training loop with KL loss to run on-policy distillation with tool use. This opens the door for a new domain of application in agentic LLM because small/medium models now have access to dense, on-policy rewards from a swarm of SOTA large models (deepseek, gpt-oss). We will next up begin our training runs and see how they compare with traditional RL/SFT on multi-turn tool use.

I’ll be attending #NeurIPS# starting Wednesday as part of @thinkymachines! Feel free to DM me if you’d like to catch up, chat about research, or learn more about Thinky (we have openings!)🤝

I recently joined @thinkymachines -- super excited to work with the team, I think we have the highest density of research talent in the world 🙂 we have a very ambitious roadmap ahead, the right team to work on it, & I think now is a great time to join; you should reach out to the team if that excites you!