Berkeley study reveals the 'harness tax': harness choice impacts coding agent cost more than accuracy

arena · x · 2026-09-29

LMArena highlights new research by Melissa Pan, PhD candidate at UC Berkeley's Sky Computing Lab, benchmarking Claude Code vs Codex vs Pi.

She introduces the hidden "harness tax": the system surrounding an AI model materially changes its cost and performance. Among three surprising findings: harness choice impacts cost more than accuracy — meaning picking the right harness matters as much as the model itself when building coding agents on realistic budgets.

Related event: Berkeley Study: Coding Agent Harness Choice Drives Cost More Than Accuracy(2 posts)→

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