In a 24-hour window in the middle of July, two labs opened the weights on two large models with the same two headline numbers — open license and a 1-million-token context. On July 15 it was Inkling, the first model from Mira Murati's Thinking Machines Lab. On July 16 it was Kimi K3, Moonshot AI's 2.8-trillion-parameter flagship. The tech press filed them under the same story: China and a US lab both shipped big open models this week.

They are not the same story. They are close to opposite ones, and if you're a team of one deciding where to spend the next month, telling them apart is the whole game.

The one-screen answer#

Kimi K3 is a scale bet. Inkling is an ownership bet. K3 is the largest model ever opened — 2.8T parameters — and its near-term value to you is a cheap hosted frontier API, not a download. Inkling is deliberately not the strongest model available; its value is that it's a 975B base you fine-tune into your own model and keep. So the real question was never "which open-weight model." It's: do you want to rent a bigger generalist, or own a specialized base? Everything below is that sentence, expanded.

Kimi K3: rent the giant, self-host later (or never)#

Moonshot's number is the one everyone repeated: 2.8 trillion parameters, 896 experts, 16 active per token, a 1M-token window, full open weights promised July 27. It is a genuine milestone — the first open-weight model in the three-trillion class. But "open" is doing less work than it looks like. At roughly 1.4TB of MXFP4 weights, K3 is a multi-GPU or rented-inference deployment, not a side project. And at launch the "open" weights weren't published yet, nor was the Modified MIT license file — a promise, not a document.

So for now the honest use of Kimi K3 is the hosted API at $3/$15 per million tokens ($0.30 cached input) — a competitive price to touch frontier-scale capability with zero infrastructure. The benchmark story is the part to hold loosely: Moonshot's coding claims lean on newer suites that aren't yet independently replayable, with no SWE-bench Verified or Pro figures at launch. We laid out the full founder playbook — prototype on the API, measure your own tasks, self-host only when volume bends the cost curve — in what a founder actually does with Kimi K3.

Inkling: own a base, not a chatbot#

Thinking Machines went the other way on purpose. Inkling is a 975B mixture-of-experts model that activates only ~41B parameters per token, trained on 45 trillion tokens of text, image, audio, and video, released under Apache-2.0 with weights on Hugging Face on day one. Artificial Analysis called it the leading US open-weights model at release — but the company says plainly it is not the strongest model available, open or closed. That's not modesty; it's the pitch. Inkling exists to be specialized through Tinker, their customization platform. The deliverable is a base you own that answers your thing better than a rented generalist does. We walked the fine-tune-and-own-versus-rent-and-prompt decision in full in Inkling: the base a founder fine-tunes instead of renting.

One lab opened the biggest model and told you to rent it. The other opened a smaller one and told you to make it yours. Same week, opposite advice.

So which one is yours?#

Strip the launch noise and match the model to the shape of your product:

The trap is treating this as a capability contest and picking the bigger number. A 2.8T generalist will out-reason a fine-tuned 41B-active base on a random hard prompt — but "random hard prompt" is not a business. Pick the bet, not the parameter count: rent the giant when you need breadth this week; own the base when your edge is depth you can keep.