Thinking Machines Lab's Inkling Hits Databricks as a 975B Open-Weights Rival
Thinking Machines Lab's first open-weights model lands on Databricks with enterprise governance, coding agent integrations, and a 975B-parameter MoE architecture
- Day-zero launch: Thinking Machines Lab's first open-weights model, Inkling, is now available on Databricks via Unity AI Gateway.
- Architecture: 975B total / 41B active parameter MoE transformer, pretrained on 45 trillion tokens of text, image, audio, and video.
- Top U.S. open-weights model: Scores 41 on the Artificial Analysis Intelligence Index, beating Nvidia Nemotron 3 Ultra (38) and leading on agentic benchmarks.
- Token efficient: Uses one-third the tokens of Nemotron 3 Ultra to match coding performance; priced at $1.87/M input tokens via Tinker API.
- Enterprise governance: Connects to Cursor and OpenCode through Unity AI Gateway with centralized security, cost controls, and audit logging.
- Open weights + Apache 2.0: Weights available on HuggingFace with a 1M token context window; fine-tunable via Tinker.
Inkling, the debut open-weights model from Mira Murati's Thinking Machines Lab, is now available on Databricks as a day-zero launch partner. The model ships through Databricks' Unity AI Gateway, giving enterprise teams a governed, customizable path to one of the most capable open-weights models to come out of a U.S. lab in years. For teams already living inside the Databricks ecosystem, this is a meaningful shortcut: no separate API keys to manage, no separate billing pipeline, and no data leaving your governed environment.
The model behind the announcement
Inkling is the first in-house AI model from Thinking Machines Lab, the startup founded by former OpenAI CTO Mira Murati. Unlike flagship models from OpenAI, Anthropic, or Google, it is open-weight, meaning companies can download and modify it directly. It is a Mixture-of-Experts (MoE) system with 975 billion total parameters, though it only draws on about 41 billion for any given task -- a design that keeps inference fast and cheap despite the enormous total parameter count.
The model was pretrained on 45 trillion tokens of text, images, audio, and video. It is also the first in a planned family: alongside it, Thinking Machines is sharing a preview of Inkling-Small, a lighter-weight model with 12B active parameters trained with a similar recipe.
Architecturally, Inkling is a 66-layer decoder-only transformer with a sparse Mixture-of-Experts feed-forward backbone, where each token is routed to 6 of 256 experts, plus 2 shared experts active on every token. That routing mechanism is what makes the 975B parameter count practical -- most of the network is idle for any given forward pass.
One standout design choice is controllable thinking effort. Inkling reasons natively over text, images, and audio, and balances cost with performance through efficient and controllable thinking effort. Teams can dial reasoning up or down depending on whether they need fast responses or deep chain-of-thought, without switching models.