Today, we are releasing Inkling-Small. Inkling-Small achieves comparable performance to Inkling at a quarter of its size. It features 276B total parameters, 12B active. We are making the full weights available. https://t.co/BtYNcpkDRA Fine-tune it on Tinker today, or chat with it in text, image, and audio on Tinker Playground.
Efficiency is the point. Across agentic tool use (Terminal-Bench 2.1), reasoning (HLE), and instruction following (IFBench), Inkling-Small delivers more performance per FLOP than Inkling. Variable thinking effort lets you pick your point on the cost/performance curve.
Inkling-Small began training after its larger counterpart, so it benefits from everything we learned: an improved pre-training data mix, a refined ML recipe, on-policy distillation with Inkling as the teacher, and two further weeks of agentic coding RL.
It matches or exceeds Inkling on reasoning and agentic tasks. 31.6% on HLE, ahead of Inkling’s 29.7%, and the advantage holds at every thinking budget. On SWEBench-Verified it exceeds 80%.
An open-weight 12B-active model that edges out its four times larger teacher on HLE and clears 80% on , with better performance per FLOP on agentic .
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