Model size and reasoning effort are two separate scaling knobs whose performance curves overlap — a smaller model run at higher reasoning effort can sometimes match a larger model run at lower effort.

Analyzing GPT-5.6 benchmark curves, Raschka finds that training-scale (model size) and inference-scale (reasoning effort) trade off against each other, with smaller high-effort models rivaling larger low-effort ones. ✦ AI generated

Sebastian Raschka · Ahead of AI · 2026-07-18 · original ↗

As expected, both approaches can improve the benchmark score, but they also increase the cost. More interestingly, the curves overlap. For instance, a smaller model at a higher reasoning effort can sometimes reach a similar score as a larger model at a lower reasoning effort.

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