Offloop多智能体超越Claude Code

So much recent work and research papers points to the same thing: the "harness" is becoming the real capability layer. @Offloop 's 4-person team demonstrated a multi-agent harness outperforming Claude Code and Codex on GDPval benchmarks, targeting $2.4T in US knowledge work. - The team scored 84.9 at $1.65 per task. - Opus 4.8 inside Claude Code scored 82.4, and GPT 5.6 Sol inside Codex scored 83.3, costing far more per task, $14.38 and $5.20 GDPval measures how well AI handles real work across 44 occupations and 9 major industries. Models get shell access and web browsing, then face blind pairwise comparisons against human experts. And those tasks map onto US jobs paying roughly $2.4T a year.  > **引用原帖 Offloop (@Offloop):** > In our internal evaluations, Offloop achieved new state of the art results on three benchmarks evaluating professional knowledge work: > 84.9 on GDPval, 44 on GDP.pdf, 67.2 on JobBench - each ahead of Codex and Claude Code, at 1/3 to 1/10 of their cost per task. > Lower cost per completed task means agents you can leave running, more tasks per dollar, and margins that survive scale. > https://x.com/Offloop/status/2080304786646433874