Inkling: what it means when a lab gives away the model.
On July 15, Thinking Machines Lab — the company founded by former OpenAI CTO Mira Murati — released its first AI model, Inkling, and published the whole thing for anyone to download. Not a demo, not an API: the actual model. This lesson explains what "open weights" means in plain English, why a company would give away its most expensive asset, and where this model will show up in your life even if you never touch it.
01 First, what "weights" are
When people say "the model," they mean a giant file of numbers. Training an AI is the process of tuning billions of these numbers — the weights — until the system gets good at predicting and reasoning. The weights ARE the intelligence: everything the model learned from training is stored in them. Copy the file, and you've copied the model.
Closed labs (OpenAI, Anthropic, Google) guard that file the way Coca-Cola guards its formula. You can talk to ChatGPT all day, but you will never see or change the numbers behind it. Open weights means the finished file is published — anyone can download it, run it on their own hardware, and retrain parts of it for their own purposes. Inkling's weights went up on Hugging Face, a public library for AI models, on day one.
You'll see these terms mixed up constantly. Open weights: you get the finished model. Open source (strictly): you'd also get the training data and code — the full recipe, not just the cake. Almost no frontier lab releases the recipe. Inkling is open weights, and that's still the meaningful part for everyone downstream.
02 What Inkling actually is
| Spec | Inkling | Why it matters to you |
|---|---|---|
| Size | 975B parameters (41B "active" per response) | It's a Mixture-of-Experts — a committee of specialists where only the relevant few answer each question. Big brain, efficient bills. |
| Inputs | Text, images, and audio | It can read documents, look at charts, and listen to recordings — one model, three senses. |
| Context window | Up to 1M tokens | Roughly a long novel's worth of material in one conversation. |
| Thinking effort | A dial, not a switch | Developers can turn "how hard it thinks" up for tough problems or down for speed and cost — per request. |
| Honesty training | Calibration & abstention | Trained so that answering only pays off when it's likely right — so it says "I don't know" instead of confabulating. |
That last row is the sleeper feature. Most models are trained in ways that quietly reward confident guessing — it's a root cause of hallucinations, which we cover in spotting hallucinations. Thinking Machines trained Inkling with rewards for calibrated answers: confident when it should be, hedged or silent when it shouldn't. A model that knows what it doesn't know is more useful than a slightly smarter one that bluffs.
03 Why give away the crown jewels?
Because the business isn't the model — it's the customization. Thinking Machines sells Tinker, a platform where organizations fine-tune models on their own data: their documents, their vocabulary, their judgment calls. Inkling is the free foundation designed to pull customers into that workshop (it launched on Tinker with a 50% intro discount, which tells you the strategy). The wider pattern matters for the whole industry: open-weights models mean companies can own their AI — run it on their own servers, tune it to their field, and never worry about a vendor changing prices or shutting an API. That option existed before, mostly from Chinese labs like DeepSeek; Inkling is one of the strongest American entries yet.
04 Where you'll actually meet it
The 60-second takeaway
- Weights = the file of learned numbers that IS the model; open weights = that file is public.
- Inkling (July 15, Thinking Machines Lab / Mira Murati) is a big, multimodal, deliberately honest open-weights model — built to be customized, not to top leaderboards.
- The business logic: give away the model, sell the customization platform (Tinker).
- For you: expect more products running "their own" AI — and try the free playground if you're curious what calibrated honesty feels like.