Today, we are introducing Inkling.
Inkling reasons efficiently across text, image, and audio modalities. We are making the full weights available.
https://t.co/Ghebq5mG30
Available today for fine-tuning on Tinker. Play with it in the Inkling Playground. 🧵
Cost and latency are important in real-world use cases. Inkling's continuous thinking effort lets you pick your point on the cost/performance curve—reaching the same score with a fraction of the tokens.
Inkling natively understands and reasons across text, audio, and images. It’s strong on audio in particular, ranking among the strongest open-weights models on VoiceBench, MMAU, and AudioMC.
We’re grateful to our partners for their day-0 support across the open-source ecosystem: @togethercompute, @FireworksAI_HQ, @databricks, @UnslothAI, @modal, @baseten, @lightseekorg, @inferact on VLLM, @radixark on SGLang.
token — The chunk of text a model reads and writes in — roughly three-quarters of a word — and the unit AI usage is billed in.
open weights — A model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.
multimodal — A model that works with more than text — reading images, audio, or video, and sometimes generating them too.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
Why it matters
Full open weightsA model whose trained parameters are published for anyone to download and run — unlike API-only models you can access but never possess.Full definition → for a natively multimodalA model that works with more than text — reading images, audio, or video, and sometimes generating them too.Full definition → reasoner with strong audio benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition → results and an adjustable thinking budget, fine-tunable on Tinker from day one.
