Thinking Machines Lab debuts Inkling, an open-weight MoE model with 975B total and 41B active parameters, trained to be broad rather than optimized for one area
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Thinking Machines Lab
Context & Ripple Effects
Thinking Machines Lab first positioned Tinker as a fine-tuning API supporting external open models, then made it generally available and added Kimi K2 Thinking support. Inkling gives the company a model of its own to place alongside that customization layer.
The lab has also framed its work around systems people and organizations can shape, including its interaction-model previews. An open-weight, broadly trained model makes that positioning more concrete by giving users a base model they can adapt rather than only a fixed endpoint.
First-order effects
Developers can obtain and evaluate Inkling through its open-weight distribution, while Tinker becomes an immediate route for adapting the model to particular uses.
Thinking Machines Lab shifts from primarily offering tooling around third-party models to offering a proprietary base model that can anchor its own product stack.
Second-order effects
Fine-tuning and model-hosting platforms will face stronger pressure to make it simple to customize, evaluate, and serve large open-weight MoE models, not merely provide access to closed-model APIs.
Organizations choosing among open models gain another broadly positioned base model, increasing the importance of downstream differentiation through data, tuning workflows, and deployment support.
Third-order effects
If labs increasingly pair open weights with managed customization tools, competition may move away from one-size-fits-all model access toward control over how enterprises adapt and operate models.
The pattern could support a more modular AI stack in which base-model providers, fine-tuning platforms, and application builders compete and partner around the same weights; whether Inkling becomes a meaningful shared foundation will depend on adoption and practical performance.
The trend: Inkling is part of the shift toward open-weight foundation models bundled with tooling that lets customers shape models for their own workflows.
Today, we are introducing Inkling. Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available. https://thinkingmachines.ai/ ... Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Thinking Machines just dropped the best open weight AI model outside of China! Inkling beats Nemotron 3 Ultra and benchmarks put it between Kimi 2.5 & 2.6. Many were contending to this throne, but Thinky has come out on top. Really solid release, and will pair well with Tinker. […
BREAKING: Inkling by @thinkymachines is 9th overall on Agentic Web App Arena by Design Arena with an Elo of 1257 It's an open-weight model in the same performance band as Claude Opus 4.6 by @AnthropicAI and Gemini 3.5 Flash by @GoogleDeepMind This makes Inkling the [image]
Inkling is our first open-weights model: 975B parameters, multimodal input, controllable reasoning effort, and available today for fine-tuning on Tinker. Proud of what we built together and excited to see what people teach it next!
Love this approach to open releases from Thinky — release a practical foundation to build on. I expect this to succeed for them, much as Tinker has exceeded many people's expectations (myself included)! [image]
Pretty detailed safety section of model card, I found this interesting and practical: “Across all areas, we concluded that Inkling did not present risk of material uplift beyond what's already available in the open-weight ecosystem.”
Thinky with a ~1T param, 41B active, apache-2 model Benchmarks are a clear step up from Nemotron Ultra (55B active), new best American model, and omni input. A bit behind GLM 5.2 on agentic benchies, and Kimi K 2.6 on multi modal Super exciting! Thank you @johnschulman2 & team [i…
first open weight thinking machine model!! 975B total, 41B active trained on 45T tokens, 1M context, multimodal in sliding window with a 5:1 ratio and 512 size, deepseek aux-free load balancing and 2 shared experts (usually people only use 1), actually curious why the model is [i…
Inkling is out today, with open weights and in Tinker. It's been fun to watch this one come together: pretraining began last winter, and starting in mid-January a small team built up the coding, reasoning, and agentic training from there. We learned a lot building it, and I hope
Excited for our first general model Inkling — open weights, 975B, natively multimodal (text, image, audio). Available on Tinker, HuggingFace and partners. It is yours to personalize and use openly. It is yours.
Congrats @thinkymachines on the new open model 🙌 Inkling was trained on NVIDIA GB300 NVL72 and the NVFP4 checkpoint is available today on @huggingface: https://huggingface.co/... Happy building!
This model is interesting because it's the seemingly best base for a full scale post train/RL run for specific workloads or generalization. Helps that it's audio/image/text in! I'd be curious to see a proper model built off this or how Thinking Machine customers use it
Thinking Machines has released Inkling, the new leading U.S. open weights model, debuting at 41 on the Artificial Analysis Intelligence Index @thinkymachines has previously released research previews of models and this is their first production language model release. The model […
So excited to see the Thinking Machines Lab team launch Inkling - their first model, with the full weights free for anyone to download, customize, and truly make their own! …
Nvidia's Nemotron is one of the closest alternatives, but it is strategically tied to Nvidia's Blackwell and NVFP4 ecosystem, whereas Inkling gives Thinking Machines a foundation model it can optimize directly around Tinker. — Check it out: thinkingmachines.ai/news/introdu...