Saturday Jul 18
STARTUPSOther

Thinking Machines Ships Its First Model

18JUL
975B PARAMSINKLING

The most-hyped AI startup finally has something to show. Mira Murati's Thinking Machines Lab released Inkling, its first model ever. It admitted openly: not the best model out there.

Thinking Machines raised over $12 billion before shipping a single product. This is the first real evidence the bet has technical substance.

The model carries 975 billion total parameters, 41 billion active per token. Training spanned 45 trillion tokens across text, image, audio, and video. On the AIME 2026 math benchmark it scores 97.1%.

Weights are open on Hugging Face, alongside the Tinker fine-tuning platform. Watch whether developers adopt it for real work instead of chasing leaderboard rank.

full brief & sources

⚡ Why this matters

  • This is the first real technical evidence behind Thinking Machines' $12B+ valuation.
  • Open-sourcing weights invites the scrutiny most well-funded labs avoid at launch.
  • Murati's candor about the model not being best-in-class is rare in a hype-heavy market.

🔍 What happened

  • Jul 15, 2026: Thinking Machines Lab released Inkling, its first in-house AI model.
  • Inkling is a mixture-of-experts model with 975 billion total parameters and 41 billion active per token.
  • It trained on 45 trillion tokens of text, image, audio, and video.
  • The model scores 97.1% on the AIME 2026 math benchmark.
  • Weights are open, available on Hugging Face, alongside the Tinker fine-tuning platform.
  • Murati's team said publicly that Inkling isn't the strongest model on the market.

💬 Smart takes

  • TechCrunch: framed Inkling as a bet against one-size-fits-all AI, aimed at enterprise customization over raw leaderboard position.
  • Skeptic: a candid 'not the best' admission is good PR, but customers still buy the best model when stakes are high.

🧭 Where this goes

  1. Likelyenterprises pilot Inkling specifically for fine-tuning use cases via Tinker, not general chat.
  2. Likelyrival labs highlight benchmark gaps to undercut the customization narrative.
  3. PossibleThinking Machines ships a stronger frontier model within 6 months to back up the positioning.
  4. Wild CardInkling's open weights get adopted as a base for a widely-used fine-tuned model outside the company.

🥄 The Spoon Take

Admitting your first model isn't the best is either confidence or damage control. Murati is betting enterprises care more about customizing a good-enough model than chasing the top of the leaderboard. That's a real strategy, not just a consolation prize.

🤔 Pushback

Enterprises say they want customization, but procurement teams still default to whichever model tops the benchmark chart when budgets get approved.

·TechCrunch - Bet against one-size-fits-all·Fortune - Murati's first model