# Mira Murati's Thinking Machines releases Inkling and admits it isn't the best model out there > Source: > Published: 2026-07-15 20:05:21+00:00 *Thinking Machines Lab just shipped its first model, and instead of chasing a benchmark crown, it's betting that companies want an AI they can actually take apart and rebuild.* Mira Murati left OpenAI as chief technology officer in September 2024. She spent the next several months raising money, not shipping products. On July 15, that quiet period ended. Thinking Machines Lab released Inkling, its first in-house model. The weights are posted openly on Hugging Face. All of them. Anyone can download it and pick it apart. Run it and you never route a single query through Thinking Machines' servers. Inkling is a mixture-of-experts model with 975 billion parameters in total. It only activates around 41 billion of them for any given task, according to the company's own announcement and reporting from TechCrunch. That's the same efficiency trick that lets massive models run without massive compute bills every time someone asks a question. Thinking Machines trained it on 45 trillion tokens spanning text, image, audio and video: no vision module bolted on afterward. The model reasons across all of those inputs natively. Hugging Face's own writeup on the release describes Inkling as the first large open model near the trillion-parameter mark to handle image and audio right alongside text, with a roughly 1 million token context window. Here's the part that matters most. Thinking Machines says plainly that Inkling is not the strongest model available, closed or open. Frankly, that's an unusual thing for a lab to admit at launch. Most releases lead with a benchmark chart. This one leads with an admission. ## Selling a base, not a black box The company is pitching Inkling less as a finished product and more as raw material. It's live today for fine-tuning on Tinker, Thinking Machines' model-customization platform, letting organizations reshape the base model around their own data instead of prompting a closed API and hoping for the best. A lighter variant, Inkling-Small, is being previewed now, with its weights coming once testing wraps up. That's a direct contrast with how OpenAI, Anthropic and Google have run their businesses. Those labs sell access to a single model tuned to serve everyone at once, updated on their own schedule, with the weights locked away. Take it or leave it. Thinking Machines is arguing that a lot of enterprise customers don't want that. They want a base they can own and retrain, then deploy on their own infrastructure, even if it means giving up a few points on a leaderboard. It's a bet with real precedent. Meta's Llama models, Mistral, and DeepSeek have all shown that open weights can pull developers away from closed APIs, especially once a company needs to fine-tune for a narrow domain like legal review or customer support transcripts. Ollama built an entire company on making those open models easy to run locally. Thinking Machines is now trying to be both the model maker and the customization layer in one package, through Tinker, rather than leaving that work to a third party. ## The money behind the model, and the open question None of this happens in a vacuum. Thinking Machines raised $2 billion in seed funding in 2025 at a $12 billion valuation, the largest seed round in history at the time, according to Bloomberg's coverage of the company. That money came in before the startup had shipped a single product. Investors were betting on Murati, John Schulman and Lilian Weng, all veterans of OpenAI's research team, not on a demo. Inkling is the first real evidence of what that bet bought. Whether enterprises actually want a model they have to tune themselves, rather than one that already works out of the box, is the open question here. Fine-tuning takes engineering time most companies don't have lying around. But if Thinking Machines is right that the frontier model race has started optimizing for the wrong thing, benchmark scores instead of adaptability, Inkling won't need to beat GPT or Gemini on a leaderboard. It just needs enough developers willing to build on top of it. Not renting someone else's black box. **Also read:** [Whatnot Acquires Shaped to Speed Up Live Shopping Recommendations](https://startupfortune.com/whatnot-acquires-shaped-to-speed-up-live-shopping-recommendations/) • [Bitcoin Miner MARA Buys Texas Power Site to Chase the AI Boom](https://startupfortune.com/bitcoin-miner-mara-buys-texas-power-site-to-chase-the-ai-boom/) • [A Caltech Startup Shrank a 27 Billion Parameter AI Model to Fit on an iPhone](https://startupfortune.com/a-caltech-startup-shrank-a-27-billion-parameter-ai-model-to-fit-on-an-iphone/)