The open-weights AI space just welcomed its most significant new entrant of 2026. Thinking Machines Lab — the company founded by former OpenAI CTO Mira Murati — released Inkling yesterday: a 975-billion-parameter Mixture-of-Experts model with full weights available on Hugging Face under Apache 2.0. No commercial restrictions. No “research only” clause. Weights you can actually run.
This is currently the strongest Western open-weights model available. And the architecture is genuinely interesting.
What Inkling Actually Is
Inkling is a Mixture-of-Experts transformer — a family of architectures that have recently become the dominant approach for scaling LLMs without proportionally scaling inference cost. The headline numbers:
- 975B total parameters with 41B active per forward pass
- 1 million token context window
- Pretrained on 45 trillion tokens of text, images, audio, and video
- Apache 2.0 license — commercially permissive, modification allowed
- Hugging Face: thinkingmachines/inkling
The MoE design means that while the model is enormous in total, each inference pass activates only about 4% of the total parameters. That’s what makes running a 975B model practically possible on reasonable hardware — you’re not paying the compute cost of 975B parameters on every token.
Architecture Highlights
Thinking Machines describes the architecture as DeepSeek-V3-inspired, with 256 expert layers plus 2 shared experts and top-k expert routing per token. This is a proven approach: DeepSeek’s MoE routing has demonstrated strong performance-per-FLOP characteristics, and Thinking Machines appears to have iterated on it with their own training innovations.
Controllable thinking effort is a standout design choice. Inkling lets users dial reasoning intensity up or down — more computation for complex tasks, faster responses for simpler ones. This is the kind of practical production feature that matters for real deployments. Running a model capable of deep reasoning at full capacity for every query is wasteful; being able to tune the effort level is valuable.
Native multimodality covers text, image, and audio — pretrained together on 45T multimodal tokens, not bolted on afterward. The distinction matters for grounding quality and cross-modal reasoning consistency.
The Agentic Coding Story
The Thinking Machines announcement puts heavy emphasis on agentic coding and tool use as a primary use case. Inkling is designed for long-horizon tasks: multi-step coding projects, one-shot web app generation with embedded browser use, and extended refinement loops for complex artifacts.
A preview of Inkling-Small ships alongside — 12B active parameters, lighter-weight, targeting deployment scenarios where you don’t want the full 41B active parameter inference overhead. This is a smart inclusion: production systems often need a fast, cheap model for triage and routing alongside a more capable one for heavy lifting.
Why Apache 2.0 Matters So Much
The licensing choice is arguably as significant as the capability numbers. Apache 2.0 means:
- Commercial use without royalties or restrictions
- Derivative model creation allowed (fine-tuning, merging, distillation)
- Redistribution allowed under the same license terms
- No “non-commercial” carve-outs that would block enterprise deployment
For context: many recently released “open” models use custom licenses that prohibit commercial use above certain revenue thresholds, restrict certain applications, or require attribution that’s functionally impossible in deployed products. Apache 2.0 eliminates all of that complexity.
This is a genuine gift to the enterprise and self-hosted AI community. Organizations that have been waiting for a capable, fully-licensed open model have a serious option today.
Where It Fits: Western Open Weights Landscape
As of this writing, Inkling represents the most capable Western open-weights model in terms of total parameters and architectural sophistication. The relevant comparison set includes:
- DeepSeek-V3 — the architectural inspiration, Chinese-developed
- Llama 4 Scout/Maverick — Meta’s MoE releases from earlier this year
- Mistral Large — strong European open-weights option
- Qwen3 — strong Chinese open-weights family
Inkling is playing in the same weight class as or above these alternatives, with an architecture designed specifically for agentic task performance and a license that beats most of the competition.
Try It Now
The weights are on Hugging Face at thinkingmachines/inkling. Thinking Machines also hosts a playground at tinker.thinkingmachines.ai for testing without local deployment.
For self-hosted OpenClaw deployments looking for an open-weights backend, this is now the most capable option in the fully-permissive license category. Hardware requirements for running 41B active parameters are non-trivial — expect multi-GPU configurations — but the Inkling-Small preview gives a more accessible on-ramp.
Sources
- Inkling: Our open-weights model — Thinking Machines Lab, July 15 2026
- Thinking Machines Inkling release — MarkTechPost, July 15 2026
- Mira Murati’s Thinking Machines releases Inkling — WSJ, July 15 2026
- Inkling model card — Thinking Machines Lab
Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260716-0800
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