Inkling's reasoning effort is a learned setting between 0 and 1, trained via reinforcement learning so the model associates higher effort with longer chain-of-thought generation, allowing it to match NVIDIA's Nemotron 3 Ultra on Terminal Bench 2.1 while producing roughly a third of the tokens.

Inkling's effort parameter (0 to 1) is trained into the model via reinforcement learning, controlling how much reasoning it does before answering, and at higher settings it achieves comparable benchmark performance to NVIDIA's Nemotron 3 Ultra with far fewer tokens. ✦ AI generated

Thinking Machines · ByteByteGo Newsletter · 2026-08-18 · original ↗

Effort is a number between 0 and 1. The documented presets run from 0 for none, through 0.1 for minimal and 0.2 for low, then 0.7 for medium, 0.9 as the default, and 0.99 at the top. The spacing between those values is uneven, which is a hint that the number represents a learned response rather than a token allowance. Thinking Machines trained Inkling's response to the effort setting during reinforcement learning, a training stage where the model produces complete attempts at tasks and receives a score for each attempt, then adjusts toward whatever scored well. During that stage, Thinking Machines varied the effort message across attempts while also adjusting the cost charged per token generated: An attempt labelled high effort could produce lengthy working-out without much penalty. An attempt labelled low effort was charged heavily for every token, so short answers scored better. Across many attempts, the connection between the message and the profitable length was learned. Higher effort encourages more reasoning without guaranteeing a longer response or a better one on any single sample. Sweeping the effort setting across its range produces a curve of score against tokens generated. On the coding benchmark Terminal Bench 2.1, Inkling reaches the same score as NVIDIA's Nemotron 3 Ultra while producing roughly a third as many tokens.

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