TypeSafe's primary spec for System One Models: RLCD training, $0.042 per million input tokens, free output, 70-500ms end to end

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The TypeSafe announcement defines System One Models as a class trained with Reinforcement Learning for Calibrated Decisions that returns typed values with calibrated probabilities through a parallel sampler rather than sequential token generation, so hallucination and type errors are structurally impossible rather than suppressed. The claimed numbers are 193.6x faster and 444.6x cheaper on production workflows, 40-200x lower latency at 70-500ms, and input metered at $0.042 per million tokens with output free. Almeida's framing is that models have been superhuman at chat for years and the automation never arrived because strings are the wrong interface for software.