Codex定制MilkDrop皮肤：怀旧视觉体验

The harder v2.1 benchmark narrows the gap between frontier models, meaning leaderboard positions from earlier versions no longer hold and model choice decisions need rechecking. Teams selecting coding agents can use the roughly 30x cost-per-task spread to weigh whether top-tier accuracy justifies premium pricing versus cheaper near-peers like Codex with GPT-6.1 Sol. Practitioners running agent harnesses will also find the anti-reward-hacking verifier updates relevant when designing their own evaluations.

Official accountArtificial Analysis@ArtificialAnlysSource

Harness architecture, not model choice, drives the spread in this evaluation — a ten-point gap on identical model and tasks. Teams building or buying browser agents should evaluate their orchestration stack separately from model selection. The benchmark's open CMU-derived task set gives developers a reproducible yardstick for comparing agent frameworks like Codex-based setups against Browser Use 1.0.

Browser Use@browser_useSource