Inkling: Thinking Machines Lab's First Open-Weight Model

Who is Thinking Machines Lab

Thinking Machines Lab was founded in February 2025 by Mira Murati, the former chief technology officer of OpenAI, alongside fellow OpenAI alumni John Schulman, Barrett Zoph, Lilian Weng, Andrew Tulloch, and Luke Metz. Murati had spent years at OpenAI overseeing the development and shipping of ChatGPT, DALL-E, and GPT-4 before leaving in 2024 and going on to found her own lab.

Within five months of founding, Thinking Machines closed one of the largest seed rounds in venture-capital history — around $2B at a$ 12B valuation, led by Andreessen Horowitz with participation from Nvidia, AMD, Cisco, and Jane Street. Rather than chasing the biggest model on the leaderboard, the lab has publicly framed its bet as building AI systems that are more customizable, more transparent about their reasoning, and cheaper to adapt — a deliberate contrast to the "one bigger model to rule them all" strategy of the frontier labs.

Tinker, then Inkling

Their Tinker platform is a production fine-tuning service — it lets developers fine-tune models without worrying about the cost or complexity of distributed training infrastructure. Inkling is their first foundation model — and they trained it from scratch, not as a fine-tune of someone else's weights.

The numbers that matter

975B total / 41B active MoE. 45 trillion tokens of pretraining — text, images, audio, and video natively. 1M token context. 30M+ RL rollouts. NVIDIA GB300 NVL72 hardware. Apache-2.0 on Hugging Face.

Honest about the leaderboard

The benchmark table is unusually comprehensive and unusually honest about where Inkling sits. Against open-weight peers it's competitive throughout. Against the closed frontier (Fable 5, GPT-5.6 Sol) it's clearly below. They say this plainly: "It is not the most performant model available today, closed or open." That admission, made in the opening paragraph, is rare.

77.6% SWE-Bench Verified — strong for an open model, matching Kimi K2.5 and GPT-OSS. 54.3% SWE-Bench Pro — mid-pack. 87.9% GPQA Diamond — competitive with the best open models. 97.1% AIME 2026 — near-saturated like everyone else. 46% HLE with tools — respectable, well below closed frontier.