Thinking Machines Lab has released Inkling, a 975-billion-parameter open-weights model published under the permissive Apache 2.0 license. The independent evaluator Artificial Analysis ranks it at 41 on its Intelligence Index, making it the highest-scoring open-weights model released by any American lab -- ahead of Nvidia's Nemotron 3 Ultra at 38 and far ahead of OpenAI's gpt-oss-120b at 24. The full weights are downloadable today.
Key facts
- The headline number: Inkling debuts at 41 on the Artificial Analysis Intelligence Index, the top score for any US open-weights model, with Nemotron 3 Ultra second at 38.
- When: Released July 15, 2026.
- Who: Thinking Machines Lab, the research company founded by former OpenAI CTO Mira Murati.
- Primary source: The lab's Inkling announcement and model card; weights on Hugging Face.
For most of the past two years, the phrase "leading open-weights model" has meant a Chinese one. Labs like DeepSeek, Zhipu, Moonshot and Alibaba have shipped free-to-download models that kept pace with, and sometimes embarrassed, the paid American frontier -- while US labs mostly kept their best work behind an API. Inkling is the most serious attempt yet to change that, and it comes from a company barely a year old.
Thinking Machines is unusually direct about why it did this. "Today we are advancing our mission by releasing a model we trained from scratch with the full weights available, so that people can make it their own," the lab wrote in its announcement. That last clause is the point. Apache 2.0 means anyone can download Inkling, modify it, fine-tune it on private data, and ship a commercial product built on it, without asking permission or paying a toll. The lab's model card frames the intent plainly: it is "released with open weights to support research, fine-tuning and integration into third-party products by downstream developers."
What is actually inside it
Inkling is a mixture-of-experts transformer. That architecture is the reason the parameter count is both enormous and slightly misleading. A dense model puts every one of its parameters to work on every word it processes. A mixture-of-experts model instead contains many specialist sub-networks and, for each token, a router picks a handful to consult. Inkling holds 975 billion parameters in total but only activates about 41 billion at a time.
The useful analogy is a hospital rather than a doctor. A dense model is one very well-read generalist who personally sees every patient. A mixture-of-experts model is a large hospital with hundreds of specialists on staff and a triage nurse at the door: the institution's total expertise is vast, but any single patient only occupies two or three people's time. You pay to keep the building staffed -- the memory cost is real -- but each visit is cheap.
The rest of the spec sheet is ambitious. Thinking Machines says Inkling was pretrained on 45 trillion tokens spanning text, images, audio and video. It accepts text, image and audio inputs and produces text. It handles up to a million tokens of context in the open-weights release, though the 256K limit applies through the lab's Tinker fine-tuning API. The lab argues the appeal is not any single number: "Instead, a combination of qualities makes it a good open-weights base for customization: multimodal capabilities, efficient thinking, and availability on Tinker for fine-tuning."
One of the more interesting findings in the Artificial Analysis evaluation is about restraint. Inkling averages roughly 25,000 output tokens per task on the Intelligence Index -- about 40 percent fewer than GLM-5.2 at 43,000 and DeepSeek v4 Pro at 37,000. Since reasoning models bill you per token of thinking, a model that reaches a comparable answer with substantially less deliberation is meaningfully cheaper to run. In a field where test-time compute has become the default way to buy performance, spending less of it for the same result is a real engineering achievement rather than a benchmark artifact.
Why it matters, and the honest caveat
The strategic significance is larger than the leaderboard position. Open weights are, as we've written before, an insurance policy -- against price hikes, deprecations, API outages, and governments deciding who may use which model. Until now, most of that insurance was underwritten in China. A US lab publishing a frontier-adjacent model under Apache 2.0 changes the political texture of the open-weights debate at exactly the moment that debate is getting dangerous.
The caveat sits inside the claim itself. "Leading U.S. open weights model" is a carefully bounded statement, and the local model community noticed immediately. Inkling beats the American field, but strong Chinese open models like GLM-5.2 and Kimi K2.6 remain competitive or better in specific coding and mathematics domains. There is also a blunter problem: almost nobody can run this. A 975-billion-parameter model, however sparsely activated, will not fit on a desk. The same week Inkling shipped, the busiest conversation on r/LocalLLaMA was not about it at all -- it was about ternary and 1-bit quantization, and the argument that the best model is the one you can actually run. Inkling raises the open-weights ceiling. Whether that ceiling is where the value is remains an open question.
Originally published on Ground Truth, where every claim is checked against the primary source.
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