$$s = g(\theta,\; H,\; e,\; \tau,\; \phi)$$

Equation 1 · Model Evaluation

OpenAI and Claude on Agentic Coding: What the Independent Evidence Actually Shows

This equation states an equality: the expressions on both sides have the same value under the article’s assumptions.

That range is worth writing as a simple decomposition, because it clarifies exactly what a published score is actually a measurement of: $s = g(\theta,\; H,\; e,\; \tau,\; \phi)$. where $\theta$ is the model’s weights, H is the harness or scaffold wrapped around it, e is the reasoning-effort or thinking-budget setting, $\tau$ is the strength of the test oracle used to grade the output, and $\phi$ is the model’s likely prior exposure to the benchmark’s specific tasks during training. A score gap between two systems is informative about $\theta$ — the thing “OpenAI versus Claude” is supposed to mean — only when H , e , $\tau$ , and $\phi$ are held fixed across both measurements. The evidence above shows that, on the…

Meanings in this article

• $\theta$: the model’s weights.
• $H$: the harness or scaffold wrapped around it.
• $e$: the reasoning-effort or thinking-budget setting.
• $\tau$: the strength of the test oracle used to grade the output.

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