The harness controls context selection, tools and tool execution, state, orchestration, permissions, verification and termination. The article says benchmark results should be understood as results for a model–harness pair, not as isolated model scores.
To illustrate the point, Cerebras reports a single controlled task in which Claude Opus 4.6 was used in Factory Droid and Claude Code to build a lightweight whiteboard app from an empty repository. Both agents passed the same five requirements. Factory finished 11 minutes sooner, used roughly half as many tool calls, and fixed a blocking hit-testing issue before reporting completion. Cerebras explicitly cautions that this is not evidence that one harness wins every task; results may vary by task and configuration.
The article connects low inference latency to a larger practical budget for iterative tool use, testing, recovery and human steering. Its measurements are an illustrative vendor-reported run, not a broad benchmark. The engineering question it raises is how harness choices affect verified outcomes, latency and tool-call cost under repeatable evaluation.