OpenAI’s former CTO Mira Murati has delivered what many in the AI community have been waiting for: a truly open frontier model—not just open weights, but a full release—trained from scratch with 975 billion parameters. Inkling, as it’s called, puts an American flag on the open‑model map alongside Chinese powerhouses like DeepSeek V4 and GLM 5.2. It’s a direct challenge to the “open washing” trend and a signal that the US still has a horse in the open‑source race.
What Happened
On July 15, 2026, Thinking Machines Lab—founded by Murati in early 2025—released Inkling, a Mixture‑of‑Experts transformer with 975B total parameters (41B active) and a 1‑million‑token context window. Pretrained on 45 trillion tokens of text, images, audio, and video, Inkling natively handles multimodal inputs and supports efficient, controllable thinking effort. At native 16‑bit precision it requires over 2 TB of GPU memory (roughly eight Nvidia B300s or sixteen H200s); an NVFP4 quantized version cuts that in half.
The company claims Inkling is competitive with top Chinese open models across a variety of workloads, though its own benchmarks show it still trails proprietary leaders like Claude and GPT. Still, it is the largest American open‑weights model ever released, and the full‑weights release is a deliberate move: Murati has repeatedly criticised the “open” label slapped on models that only offer API access or restricted weight versions.
Alongside Inkling, Thinking Machines previewed Inkling-Small (12B active parameters) and its customization platform, Tinker, which lets developers fine‑tune the model for specific use cases. The full model card, weights, and research are available on Hugging Face.
My Take
This is a watershed moment for open‑weight AI. For months the conversation has been dominated by Chinese labs and a handful of American “open” releases that are anything but—often hobbled with restrictive licenses or missing training data. Inkling changes the calculus: it’s a genuine frontier‑class model, trained from scratch, with all weights available. Developers no longer have to rely on DeepSeek or GLM to get competitive open performance.
What makes Inkling stand out is not just its size but its pragmatism. The 41B active parameters via MoE means you can run it on a single node with high‑end GPUs, and the quantized variant lowers the barrier further. Combine that with native multimodal support and a 1M context window, and you have a model that’s immediately useful for real‑world tasks—code generation, document analysis, video understanding—without being locked into a single provider’s ecosystem.
Of course, the catch is that open doesn’t automatically mean safe. Models like Inkling can be fine‑tuned for malicious purposes, just as they can be adapted for beneficial ones. Murati’s team is betting that the benefits of openness outweigh the risks, and they’ve designed Inkling with “extending human will and judgment” in mind. That’s a refreshingly clear stance in an industry that often hides behind safety theater.
What to Watch
- Customization economy: With Tinker and full weights, expect a wave of niche‑optimized Inkling variants for healthcare, legal, finance, and creative tools. The platform could become the WordPress of AI modding.
- Regulatory pressure: A 975B open‑weights model will test export controls and safety frameworks. Watch for government reactions—especially around training data provenance and dual‑use capability.
- Response from Big AI: OpenAI’s GPT‑5.6 and Anthropic’s Claude remain ahead, but Inkling closes the gap on cost and openness. If adoption climbs, we may see proprietary labs open some of their own smaller models to retain developer mindshare.