Inkling 2 Small 模型：在 68 小时内训练 12T tokens，击败 Muse Spark

There was a time in the early days of Thinking Machines,post 2025 startandcofounder departures, when their going concern as a neolab was very much in doubt. Those days are clearly behind us, asTinker(nowa year old!) andInteraction ModelsandInklingandother high-impact contributions to AI researchhave converted Thinky’s highly credentialed potential into actual concrete ships that establish that they are now a high-functioning neolab at the US frontier.

Anyway, here’sInkling 2 Small- pretrained from scratch in less than 3 days on <12T tokens native NVFP4, withsingle joint run RL, competitive with and often beating same-size Chinese models,with VERY good vision encoder:

People are of course comparing with Western models like Mistral, Reflection and Meta Muse. People are calling it “the smartest western open weight model by a margin”, and while it is certainly competitive in that league, what’s probably the most interesting is the intentional “customizability” of Inkling, which leans into the overall Thinky and Tinker messaging:

And of course don’t miss John Schulman’s take on the alignment process for scaling model training:

AI Twitter Recap

What happened

Thinking Machines released Inkling 2 Small, a 594B-total / 17B-active multimodal MoE. It is available now on Tinker, and Apache 2.0 weights are promised "next week" after external safety testing.

• Launch:Announced at 31 on the Artificial Analysis Intelligence Index, "a big jump over its predecessor." Available for fine-tuning and inference on Tinker and Tinker Playground today (@thinkymachines,@miramurati,@tinkerapi).

• Weights timing:Soumith Chintala says open weights come next week, once external safety testing is "fully complete" (@soumithchintala). Victoria Lin confirms Apache 2.0, next week (@VictoriaLinML). Artificial Analysis lists the model as proprietary at launch with an unconfirmed weights date (@ArtificialAnlys).

• Pricing:

  • $0.30 input / $1.00 output / $1.50 training per 1M tokens on Tinker (@cHHillee).

• $0.03 per 1M cached input tokens (@ArtificialAnlys).

Training and architecture facts (official)

• Size and modalities:594B total / 17B active MoE. Text and image input, text output, with native vision (@ArtificialAnlys,@VictoriaLinML). Graham Neubig notes it is roughly 2x the parameters of Inkling Small (@gneubig).

• Context:1M context window and 1M max output tokens. Vals ran it at temperature=1, top_p=1, reasoning effort 0.99 (@ValsAI).

• Pretraining:

  • 68 hours on GB300s (@soumithchintala).

• The main pretraining phase was 8.4T tokens, run natively in NVFP4. Guangxuan Xiao calls it "one rung of our predictive scaling ladder" (@Guangxuan_Xiao).

• Horace He: "just under 3 days to pretrain" (@cHHillee). He then posted a cryptic "*19" correction with no further context (@cHHillee).