I love speed. We all love speed. But what happens when the speed is slowing you down?

Model selection for autonomous agents should be based on time to a validated correct result rather than intelligence benchmarks or token generation speed alone, because faster but less capable models may use compiler errors, tests, and tool feedback to make multiple corrective attempts within the time a slower model needs for one. Using Qwen dense and mixture-of-experts models, as well as Q4 versus Q5 quantizations, the discussion illustrates how reliability, inference speed, hardware constraints, feedback quality, and the cost of mistakes can alter which configuration completes work fastest. It argues that lower precision or faster architectures are most advantageous when failures are obvious and cheap to repair, while higher-quality models can be faster overall when they prevent cascading errors, subtle defects, or expensive backtracking. To evaluate these tradeoffs, the author proposes a Persistent Challenge Completion benchmark that gives agents objectively verifiable tasks, identical tools and environments, and fixed wall-clock budgets, then measures validated completion, time to success, recovery after failure, progress efficiency, resource use, and behavior across difficulty tiers. The central conclusion is that practical performance depends on validated work per second, incorporating initial capability, output quality, speed, environmental feedback, recovery ability, and failure costs rather than any single measure of model quality or throughput.

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