Newly released models repeatedly appear near the top on the @terminalbench 2.1 leaderboard. Are these models actually on par with frontier models on terminal tasks? We took the top 20 models from that leaderboard and ran them on TB-fn. TB-fn reworks the same 89 tasks by adding steps, tightening requirements, or requiring more complex algorithms. It also removes opportunities for shortcut solutions and ensures verifiers check only the intended criteria. Models that clustered together on TB-2.1 spread across more tiers on TB-fn. That leaves GPT-5.6 Sol and Opus 5 in the lead, while some others drop significantly in performance. At the same time, models work 38% harder on average in TB-fn, leading to a redrawn Pareto frontier of performance vs. cost. The full post includes interactive figures showing how the gap for open-weight families widens. It also measures how security refusals affect scores across Anthropic, Google, and OpenAI models. Fable 5 stands out, refusing at least once on 13 of the 89 TB-fn tasks.