Wolfram Ravenwolf argues that a benchmark measures a configured system, not a model in isolation. The dataset version, evaluation harness, agent, inference provider, sandbox and settings all affect the result. He warns that provider routing, resource limits, timeouts and retry policy can make nominally identical comparisons misleading.
Wolfram Ravenwolf builds a small evaluation using Terminal-Bench, Harbor, an agent harness and a cloud sandbox. The demonstration runs one Git-recovery task, which he reports as successful, before importing earlier complete evaluations into the WolfBench toolkit. The single live task is an illustration of the pipeline, not a complete model benchmark.
Wolfram Ravenwolf separates average performance from best and worst runs, tasks solved at least once and tasks solved in every repeated run. He adds token use and cost to explain why a lower-priced model can be more expensive for a particular workload. These measures describe the sampled benchmark runs rather than guaranteeing reliability on future or unrelated tasks.
Wolfram Ravenwolf uses reported reasoning-budget and refusal examples to show why surprising scores need trace-level investigation. More reasoning can consume the available time without completing the task, while infrastructure errors should be distinguished from agent-caused failures. He advocates recording comparable configurations and inspecting the actual trajectories before treating a leaderboard position as evidence of suitability.
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