Thinking Machines Ships Inkling: 975B Open Weights, and It's Behind Two Chinese Labs

Mira Murati's lab finally shipped a full LLM, and it's Apache 2.0.

Inkling is 975B total parameters with 41B active in a MoE configuration, multimodal on input (text, image, audio) and text out, trained on 45 trillion tokens. The context number is the fun part: 1M tokens in the open weights, 256K via their own API and Tinker. The open release is more capable than the hosted one on context length, which is a choice I'd love to hear the reasoning behind. A smaller Inkling-Small (276B-A12B) ships alongside. Weights are on Hugging Face, with day-one availability on Databricks, Baseten, Modal, and vLLM. Hugging Face has the launch post.

The architecture is genuinely odd in ways worth reading the card for: non-RoPE relative positional encoding, a 5:1 local-to-global sliding-window attention ratio, short convolutions, and 2 shared expert sinks. That's a lot of deviation from the standard recipe for a first release. TML clearly spent their independence on architecture research rather than scaling the known thing, which tracks with everything the lab has said about itself.

Now the part that matters strategically. Inkling ranks #41 on the Intelligence Index. That makes it the leading American open-weights model. It also puts it behind GLM-5.2 and Kimi K2.6. The best open model America has is third, behind two Chinese labs. It does hit #9 in the Agentic Web App Arena with notably concise reasoning and strong tool calling, which for builders is arguably the more relevant number, since agentic tool use is what you're actually deploying.

This connects directly to the Siegel Endowment paper published in Fortune this week, arguing governments and nonprofits should fund open source AI. It hit 243 points and 86 comments on Hacker News, and Inkling is the argument's best exhibit. Every significant open-weight release to date has been a byproduct of some company's competitive strategy. Meta's, Alibaba's, Mistral's, now TML's. Strategy changes. When Meta decided open weights no longer served them, the open ecosystem lost its anchor overnight. Siegel's point is that an ecosystem whose existence depends on the strategic convenience of four companies isn't an ecosystem, it's a marketing budget.

What builders should do: pull Inkling-Small (276B-A12B) before you touch the big one. 41B active on the flagship still means you need serious hardware, and the small variant is where you'll find out whether the architecture's quirks help or hurt your workload. The 1M context in open weights is the real gift here, because that's the constraint that usually forces you back onto a hosted API. If you've got a document-heavy pipeline that's been paying per-token for long context, this is worth a weekend of benchmarking.

Also, hold the "American open weights are back" framing loosely. Third place is third place.