Enterprises looking to move more of their agentic AI workloads to open weights models they can customize, control and run on-premises or in virtual private clouds have a strong new contender to consider.Today, Thinking Machines—the highly capitalized American AI startup founded by former OpenAI CTO Mira Murati—released Inkling, its first major language model under an enterprise-friendly Apache 2.0 open source license, and it boasts high, if sub state-of-the-art, performance for open weights models on third-party benchmarks, specifically software engineering (77.6% on SWE-bench Verified, where it beats fellow U.S. open rival Nvidia Nemotron 3’s 71.9%) and voice understanding (91.4% on VoiceBench compared to 94.4% for Gemini 3.1 Pro on high reasoning effort).Another differentiator: Thinking Machines notes that Inkling was designed “to answer directly on topics that may be subject to censorship,” offering enterprises concerned about factual outputs, irrespective of controversy or sensitivity, a more trustworthy option. Coming in at 975 billion total parameters, Inkling is a natively multimodal, open-weights Mixture-of-Experts (MoE) system capable of reasoning across text, images, and audio. The weights are already available on Hugging Face and the company’s own model training application programming interface (API), Tinker.Designed to balance cost against performance through a novel “controllable thinking effort” mechanism, the model represents a significant departure from the black-box scaling strategies of frontier competitors.Alongside the flagship model, Thinking Machines also announced a preview of Inkling-Small, a lighter 276-billion-parameter alternative optimized for workloads where low latency and cost are paramount.Benchmarks Show a Powerful, High-End, Sub State-of-the-Art ModelWhile Inkling is a formidable multimodal engine, it lands in a fiercely competitive 2026 open-weight land …
